HK1097243B - Elevator door monitoring system and method - Google Patents

Description

Elevator door monitoring system and method

Technical Field

The present invention relates to fault management of computer controlled doors in elevator systems or other systems containing such components.

Background

The mechanical system in normal operating conditions contains a certain amount of friction due to friction that resists movement. If the value of the friction in the system can be determined by measurement or mathematical operation, this information can be used as an indicator of the operating state of the system.

Elevator systems contain many components that are subject to friction and wear. The movement of the elevator car causes wear of components including e.g. the elevator ropes and the guide rails of the elevator car. One such component is an elevator door, which moves automatically on horizontal guide rails. It is subjected to forces applied to it from different directions and its upper and lower edges are in contact with the guide rails that keep the door moving on its track. There is also a friction force that resists the movement of the automatic door. The operation of the door can be disturbed when a sufficient amount of dirt has accumulated on the door rail on the door sill of the elevator car. Due to this physical obstruction, the force resisting the movement of the door may increase to such a large value that eventually the door control system is no longer able to open or close the door.

The value of the friction force cannot be directly measured. It is not possible to mount a separate "tribometer" on the door. The value of the friction force counteracting the door movement has to be measured indirectly. It is possible to create a model of the system to be checked, in this case the elevator door, to study the forces applied to the door. One of the forces that appears in the model is the friction that resists movement. Using this model, it is possible to calculate the desired parameters when the values of the forces opening and closing the door are known and the acceleration or velocity of the door is measured. In this way, unknown parameters such as friction can be solved. Therefore, the matter that will come is the problem of parameter optimization and estimation.

For example, in an elevator system, the door assembly includes a car door that moves with the car and landing doors that are installed on different floors. Modern automatic elevator doors are opened and closed by means of a direct current motor. The torque produced by a dc motor is proportional to the motor current. The energy of the motor is transmitted to the door, for example via a toothed belt, and the door is moved on rollers. For safety reasons the landing door is closed solely by means of the closing device without the use of an electric motor. The closing force of the closing device is generated by a closing weight or a helical spring. The motor current and the corresponding torque can be measured from a door control card (card) or directly from the motor conductors. It is also possible to monitor the so-called tacho pulse signal of the motor. The tacho signal is a square wave with a frequency dependent on the motor speed and thus on the door speed.

A problem associated with prior art solutions is that the friction acting on the elevator door cannot be measured directly. This necessitates the use of an indirect method of estimating the friction value. The value of the friction is required for estimating the time at which the door fails or for predicting the future time at which the operating state of the door will fall below a level according to a given standard.

Disclosure of Invention

The object of the present invention is to detect the operating state of an electrically operated automatic door used in an elevator system or in some other system by continuously monitoring the value of the friction force that resists the movement of the door.

One aspect of the invention relates to a method for monitoring the condition of an automatic door in a building, characterized in that it comprises the steps of: measuring acceleration or speed of the door and torque of a door motor driving the door; creating a dynamic model of the door that includes forces acting on the door as part of the model; modeling acceleration or velocity of the door by using a dynamic model of the door; calculating an error term as a difference between the measured value and the estimated value of the acceleration or velocity of the door; calculating a friction force applied to the door by minimizing the error term or a formula derived from and including the error term; and deducing the operational state of the door by comparing the calculated friction force and its change with a reference value.

Another aspect of the invention relates to a system for monitoring the status of an automatic door in an elevator or building, the system comprising: at least one door that slides horizontally in its installed position; a control system for opening and closing the door; characterized in that the system further comprises: means for measuring the acceleration or speed of the door and the torque of the motor driving the door; a dynamic model of the door comprising forces acting on the door; means for modeling acceleration or velocity of the door by using a dynamic model of the door; means for calculating an error term using information about the measured and modeled acceleration or velocity of the door; means for calculating the frictional force applied to the door to minimize the error term or an expression derived from and containing the error term; and means for inferring the operating state of the door for comparing the measured friction and its change with a reference value.

Inventive embodiments are also presented in the description part of the present application. The inventive content disclosed in the application can also be defined in other ways than is done in the claims below. The inventive content may also consist of several separate inventions, especially if the invention is considered in the light of explicit or implicit sub-tasks or in respect of advantages or sets of advantages achieved. In this case, some of the attributes contained in the claims below may be superfluous from the point of view of separate inventive concepts. Within the framework of the basic concept of the invention, features of different embodiments of the invention can be applied in conjunction with other embodiments.

The method of the invention can be used for real-time inspection of the status of automatic doors of elevators or more generally of automatic doors in buildings. More precisely, automatic doors are horizontally sliding doors which are controlled by a motor and whose closing movement is possible by means of a closing device. The door is subjected to various forces and we are now particularly interested in the values of the frictional forces applied to the door therein. From this friction it is possible to deduce a timely need for maintenance and, in less severe cases, information about the most usable friction, to anticipate the future time at which the maladjustment will most likely start to occur in the operation of the door. The operating state of the closing device of the door can be determined immediately.

In a method embodiment of the invention, the speed of the automatic door is measured. This can be done by using a so-called tacho signal obtained from the door motor. The tacho signal is a square wave in which the distance between the pulses depends on the speed of the motor and thus on the door speed. The speed of the door can be calculated from the tacho signal. A substantial part of the method is a dynamic model of the door. Some parameters in the model are updated after each complete gate sequence. A full door sequence means an opening and closing operation of the door in which reopening does not occur during the closing movement. The model includes the door and the closure device and the forces applied to these parts, including the friction forces. Using this model as an aid, the acceleration of the door is estimated, and thereby the door velocity as a function of time. The measured and estimated instantaneous velocities are compared to each other and an error term is obtained. At each instant, the error term is a function of three variables (the mass of the door, the frictional force applied to the door, and the force generated by the tilting of the door). Next, the sum of the squares of these error terms is calculated, with each square of the error terms being weighted by a desired weighting coefficient. For the so-called squared error term thus obtained, the minimum is found, in which case the three model parameters that are most consistent with reality are searched for. From the value of the friction force thus obtained, the current condition of the operating state of the door can be deduced.

In another embodiment of the method of the invention, the acceleration of the door is measured using an acceleration sensor placed on the door. The method proceeds as described above, except that the quantity estimated in the dynamic model is acceleration. In the calculation of the error term, the instantaneous acceleration estimated from the model is subtracted from the instantaneous measured acceleration. In this embodiment as well, the error term is a function of the three variables described above, and further processing to determine these parameters proceeds as in the example described above.

The input parameters required for the dynamic model of the door are the door speed, the current of the motor driving the door, the torque coefficient of the motor, the motor friction, and the mass of the door closing weight or the force factor of the closing spring.

This calculation can be simplified by defining the mass of the door as a constant value among the variables. In this case, the mass of the door is determined by taking an average from the expected number of door operations, in conjunction with the start-up or activation of the system. The length of the "teaching period" to be checked may be, for example, about twenty door operations. Once the mass has been determined as an average value as a result of the teaching period, the mass of the door can be set to a constant value. Thereafter, only a function of two variables (door friction and force caused by the tilting of the door) is processed in the optimization logic, so that the processing requires less computational performance and time than the above-described method. The mass of the door can be defined as a constant value because it can be assumed that the mass of the door does not change significantly under normal operating conditions.

For the immediate detection of a failure of the door closing device it is possible to use a Genetic Algorithm (GA). Via the GA, the correct door system model (with or without closing device) and the unknown forces related to the friction and tilt of the door can be determined simultaneously. The parameters of the dynamic model of the gate are encoded into the chromosome (chromosome) of the genetic algorithm. In this connection, the unknown parameters relating to the operation of the closing device, to the friction force applied to the door, and to the force caused by the angle of inclination of the door are genes, in other words, they together constitute a chromosome. The chromosome quality function is a squared error function that can be considered an indicator of the performance of the solution or the phenotype represented by the chromosome. Using different gene values or alleles, correspondingly different phenotypes are obtained, from which the GA optimizer finally selects the phenotype giving the minimum value as the search result. The gene value corresponding to this phenotype indicates the state of the door system at the moment of examination.

One of the advantages of the method according to the invention is that information about the operation of the door can be saved. In this way, a database is created that covers the operational history of the door, on the basis of which it is possible to plan, for example, the appropriate date for the next maintenance. From this operation history, the current situation of the door operation can be directly deduced, and even the probability of failure at a future point in time and the necessity of performing maintenance can be predicted.

Drawings

Figure 1 shows a dynamic model of an automatic door according to the invention;

FIG. 2 illustrates a method for determining unknown parameters in the model in accordance with the present invention;

FIG. 3 illustrates another method for determining unknown parameters in the model in accordance with the present invention; and

fig. 4 shows a third method according to the invention for determining unknown parameters in the model.

Detailed Description

In order to determine the frictional force acting on the door, a dynamic model of the automatic door is created, in which the force applied to the door is observed. The dynamic model of the door is shown in fig. 1. The basic law used here is newton's second law whereby the force applied to an object is obtained as the product of the mass of the object and the acceleration. Another basic law relating to friction gives the value of the friction resisting the movement of an object, which is the product of the coefficient of friction and the force with which the object is pressed against the surface under examination (for an object sliding on a smooth surface, gravity). For clarity, in this dynamic model, it is assumed that all moving masses are concentrated on a single particle m_door10, respectively. Accordingly, all of the friction forces present in the system, except for the motor friction force, may be combined into a single concentrated friction force term F_μ，door. The dynamic operational model of the door system may be created using five forces acting on it, these forces being: the force of the motor, the force caused by the closing weight or spring, the force caused by the tilt angle of the door, the internal friction of the motor, and the friction caused by the door itself. The total mass of the system comprises the concentrated mass of the door 10 and possibly the mass of the closing weight 11. The entire moving mass contained in the door mechanism is concentrated into the door mass 11. FIG. 1 shows the mass points and forces in the system, as well as the positive direction of velocity and acceleration.

Obtaining the instantaneous acceleration for the door 10 according to a dynamic model and Newton's second lawExpression (c):

wherein when the closure device is a counterweight, F_motor＝Bl-I_motor(t) and F_cd(x_d(t))＝m_cdG, and when the closure device is a spring, F_cd(x_d(t))＝k_cd·(x_d0+x_d(t)). Bl is the motor torque coefficient, I_motorIs the motor current, F_motorIs the force caused by the motor, F_tiltIs the horizontal component of the force caused by the tilting of the door, and F_cdIs the force caused by the closing device, F_μMotorIs the internal friction of the motor, F_μDoorIs a concentrated friction force, m, acting on the door and caused by all the auxiliary parts_doorIs a common lumped mass comprising the masses of all departments, and m_cdIs the mass of the counterweight. If the closure device is a spring, m_cd0. Since closing weights are more commonly used as closing devices, in the following, we will only deal with closing weights. However, this does not limit the device of the present invention to only a closing weight, which may be a mechanism that derives a closing force from a spring or other configuration.

When a sample of the quantity to be measured on the door is taken by means of the device of the invention to determine the friction, this means a transition from a continuous time domain to a discrete representation. In this case, (1) changes to the following form:

where the instant t has been replaced by the sample with the sequence number k taken at this instant.

Of the parameters of the door dynamic model, the mass of the closing weight, the torque coefficient of the motor and the internal friction couple of the motor must be known in advance. The mass of the closing weight can be easily determined by weighing. The motor torque coefficient and the internal friction couple can be determined by means of a dynamometer. Using a dynamometer, the motor torque can be measured as a function of the motor current. The results obtained with different current values form an approximate straight line T, for which the equation is as follows:

T(I_motor)＝Bl·I_motor-T_μMotor-T_μDyn (3)

where T is the motor torque. By linear regression, the unknowns Bl and T can be combined_μMotorDetermined as the slope of the regression line and the intersection of the regression line with the Y-axis.

By considering the power transmission mechanism of the door system, the force acting on the door can be obtained from the motor torque. In the example, the motor shaft carries a pulley of radius r, and a toothed belt running around the pulley moves the door leaf. In this case, a force F to move the door leaf can be easily obtained_motor＝T/r。

On the other hand, from this model it is possible to determine the unknown parameters, which in this connection are the door mass, the friction caused by the inclination and the friction acting on the door. Of these parameters, the last-mentioned parameter is the object of interest in the preferred embodiment of the invention.

A method for determining unknown parameters according to the present invention is given in fig. 2. The movement of elevator doors 20 is controlled by control logic 26 that receives commands therefrom to open or close the doors. The door is driven by a dc motor connected to a door control card. The motor current and the so-called tacho signal can be measured directly from this card. The tacho signal is obtained from a tacho sensor (generator) of the motor that detects the mechanical rotational speed of the motor. In this embodiment, the tacho signal is typically a signal having a square wave shape. The frequency and pulse interval of the square wave are proportional to the speed of the door motor and the speed of the door. Between two consecutive pulses the gate always moves the same sub-distance dx.

Will control the slaveThe signals received by the card and the commands given by the control logic are transmitted to a functional block 21, which is responsible for the collection and pre-processing of the information. In this block, the door motion data is filtered to remove from it those door opening operations during which the door has to be re-opened during the closing action because of an obstacle, usually a passenger, in the door path. During the period dt between two tacho pulses, the door moves over a constant sub-distance dx. In block 21 it is now possible to calculate the speed v of the door at the instant k of each time_d：

The pre-processing block also calculates weighting coefficients for the calculation of the later error terms. By using weighting coefficients, the desired error term can be weighted more than the other terms. In a pre-processing block 21, all information related to the door opening and closing operations is combined for further processing.

The next step in the method is the processing of the dynamic model 22 of the door. The model is as described above and illustrated in fig. 1. As mentioned above, the input parameters into the model are the motor torque coefficient, the friction couple of the motor, the mass of the door closing weight, the motor current, the period of time dt, and the speed v of the door_d. In this model, the acceleration of the door is estimated as a function of four variables, which are shown below.

Where Σ F_k(. cndot.) is the sum of the forces acting on the door at instant k. From the estimated door acceleration, the speed of the door can be estimated as follows:

wherein v is_d，0Is the speed of the door at the instant t-0.

In the next step, the estimated speed of the door and the door speed calculated in the preprocessing block are passed into a differencing block 23. Subtracting the estimated instantaneous speed from the measured instantaneous speed to produce an error term e_kAs a result. Error term e_kIs three variables m_d、F_μAnd F_tiltAs a function of (c). By applying a weighting factor W_iNow, the so-called squared error term E can be calculated in block 24:

in the next step in the block diagram of the method of the invention, the squared error term E is passed to the optimizer 25. The function of the optimizer is to minimize a function (7) of the three variables. When the minimum value is found, the variable parameters corresponding thereto have been estimated for the mass of the door, the friction force resisting the movement of the door and the force caused by the tilting of the door.

In the examples shown in fig. 2-4 and the model shown in fig. 1, it is possible to define one or more force parameters in the model as constant values if it is desired to simplify the model and the calculations according to certain assumptions.

Fig. 3 gives another example of the method in the invention for detecting automatic door failure. The operation in this example is very close to the method shown in fig. 2. The control logic 36 of the elevator system issues an open or close command to the door. In the case of elevators in which no motor tacho signal is available, the movement of the elevator door must be observed by other means. One method is to install an acceleration sensor on the door leaf 30 to monitor the acceleration of the door. The measured acceleration a_dTo a block 31 for collecting and pre-processing information. As in the above-mentioned block 21, the door movement data are filtered to remove therefrom those door opening operations during which the door has to be re-opened during the closing action because of an obstacle in the door path. Thereafter, the door speed v is calculated in block 31 according to the following basic formula_d：

Wherein v is_d，0Is the initial velocity of the door at instant t-0. In other respects, the pre-processing block 31 operates as the pre-processing block 21 in fig. 2. The signals between the block 31 and the dynamic model 32 of the door are in accordance with the method of figure 2, except that the difference in the error term E is calculated from acceleration values rather than from velocity.

In the model 32, the estimated door acceleration is calculated according to equation (5). This information is fed directly to the difference block 33, in which the measured acceleration, in this case obtained from the sensor, and the estimated acceleration obtained from the model are subtracted from each other. This produces an error term e, which is a three-variable function of the same type as the example of fig. 2. In the manner described above, the error is squared with the desired weight in block 34. Accordingly, optimizer 35 functions in the same manner as optimizer 25. Thus, the same three unknown parameters are obtained as described above.

In an embodiment of the model, three unknown parameters in the model are determined once in connection with the start-up of the system. To ensure the accuracy of the parameters, several door operations are required per floor. A suitable estimate of the number of door operations is at least ten. When the system is subsequently in its working state, the model previously defined by the system is used, and this makes it possible to compare the existing model with recently collected new information about the door movement. After this comparison it is possible to conclude, for example, that the friction force F is_μWhether or not it has changed significantly. According to the error term e_kThat is, the friction force significantly increased between the door and the door rail is rapidly detected from the remainder of the model.

The residuals in the model may be analyzed, for example statistically. It is possible to calculate, for example, the mean, variance, distortion of the distribution, and the number of peaks. The error term may also be analyzed with respect to frequency range. By these analysis methods it is possible to determine the typical characteristics of different fault situations. For example, an increase in the frictional force resisting door movement will appear as the average value of the residuals deviating from zero. In order to analyze the fault type on the basis of the statistics or frequency range signals, it is naturally required that the fault type can be clearly distinguished from each other and from an error-free operating state by examining the amplitudes and frequencies of the spectral components. This can be difficult.

In another embodiment of the model, the analysis of the operational state of the door is preferably performed each time the door is closed or opened. The method in this case is a continuous detection method. The processing and analysis of the collected information must be performed in the time period between two door operations. In the case of elevators, this treatment period should be a maximum of about 15 seconds, which is the time required by the elevator in a travel cycle between two successive floors. Of course, it is not absolutely necessary to include every gate operation in the analysis. Thus, it does not matter even if the analysis of one door operation would require more than about 15 seconds as described above. In this case, the efficiency of the fault diagnosis is naturally impaired. Even if not including every door operation in the analysis, it is still important to count the number of all floor-specific door operations. This is a necessary item of information when the average age of the door is to be determined in the event of a failure.

The analysis performed by the optimizer can be simplified by assuming that the mass of the door is a constant value. In any case, the mass of the door must be defined in connection with the start-up of the system. In practice, the model is given a constant door mass value, which is determined to be, for example, the average of the mass values obtained from the first 20 door operations at each floor. After this "teaching phase", the function of the optimizer is to find values for two unknown parameters, namely the friction force resisting the door motion and the force caused by the tilting of the door. The computational effort is now reduced and the search for parameters is made easier. After this teaching phase, the method in this example of the invention functions similarly to the method shown in fig. 3, with the difference m_dIs now a fixed parameter, and e_kAnd E is a function of two parameters.

A typical door failure condition is, for example, a failure occurring in the bearings of the roller guiding the door, which prevents the door from sliding smoothly on the roller. In such a case, the door mechanism failure friction force F depends on the nature of the failure_μEither abruptly or slowly over time. One possibility is to determine the need and time for maintenance based on this information.

Another possible type of failure is a failure of the door closing device. Such a malfunction may occur, for example, when the closing weight has been removed at the time of maintenance and the mechanic forgets to install it again. Failure may be caused by a break in the wire rope closing the weight. Such a failure is manifested as a force F caused by the tilting of the door_tiltIs suddenly and largely increased. It can be concluded that such a large tilt of the door is not due to an actual tilt, but to the disappearance of the closing force. In this respect, a need arises for a process of inferring the operating state of the closure device to be performed automatically by a suitable method. The genetic algorithm canFor this purpose. Using these algorithms it is possible to determine the correct door model, either with or without closure devices included, and the unknown force F_μDoorAnd F_tilt. In searching for friction and tilting forces, the genetic optimizer also finds the system model that produces the smallest tilting force at the same time.

Genetic algorithms are based on the principle of creating artificial evolutions through the use of computational logic of a processor. The question of interest is how to obtain the final result ("phenotype") as advantageously as possible by changing the attributes of the "population". In the variation process, the genetic manipulations used are "selection", "hybridization" and "variation". The strongest members of the population "reach the predetermined target" and the attributes of these members are passed on to the next generation. In an example of the method of the present invention, the population is a plurality of model parameter vectors. In this regard, one parameter vector corresponds to one chromosome. Each chromosome has a gene. In this regard, each gene corresponds to one of the model parameters to be estimated, which is now the operation of the closing device, the friction of the door, and the tilting force of the door. These three genes together may be referred to as a phenotype. The operation of the genetic algorithm is such that populations are first created using randomly selected gene values. For each chromosome in the population, an "efficiency" or quality value is calculated, which in this example is the above-mentioned squared error term calculated from the dynamic model of the gate. In genetic algorithms, searches are performed generation by generation. The most efficient chromosomes, i.e. those giving the lowest squared error term values, are selected from each generation and included in the next generation. The next generation is created via hybridization and mutation according to the best alternative after this selection. As a result of the genetic manipulation, new population types are obtained in which the genotype of the chromosome differs from the previous population entirely or only for certain genes. For the new generation, the efficiency, i.e. the squared error term, is calculated and as a result the chromosome with the best efficiency is obtained again. Thereafter, the array of squared error terms is checked to see if it converges or if a sufficient number of generations have been processed to ensure convergence. As a final result, the genes of the best individual in the last generation reveal the value of the unknown force and the operating state of the closure device.

The operation of the genetic algorithm described above can be combined with the block diagrams 2 and 3. The block diagram 4 gives by way of example the operating principle when the genetic algorithm is combined with the block diagram 2. In the automatic door 40, the current of the door motor and the tacho pulse signal of the motor are measured. In a pre-processing block 41, the speed of the door is calculated and the result is passed to a difference block 43 and a model 42 of the door. In this example, the mass of the door is assumed to be a constant value. In this model, the speed of the door is estimated and likewise passed to the difference block 43. The squared error term calculator 44 and the so-called GA optimizer 45 form a loop that operates as described above in connection with the description of the genetic algorithm. Information about the genes is passed from the GA optimizer 45 to the error calculator 44 and correspondingly the efficiency value, i.e. the squared error term E, is passed from the error calculator 44 to the GA optimizer 45. As a final result of this search, the optimizer gives the parameters CD, F_μDoorAnd F_tilt. CD means the operational state of the closing device, wherein for example a value of 1 may indicate a non-faulty operation of the closing device, whereas a value of 0 indicates a fault of the closing device. These three parameters are returned to the model so that the model can immediately take into account the performance of the closure device. Thus, in addition to the force parameters, a model that best describes the system is immediately found. Door open and close commands come from the door control system 46. The dynamic model of the door is now:

where the term CD is 1 when the closure device is in operation and 0 when the closure device is not in operation. In order for the genetic algorithm to be able to find the system model that produces the smallest inclination, the inclination force F is also used_tiltIncluded in the error function is:

where K is the scaling factor, G is the sequence number of the generation in the genetic algorithm, and G1 is the limit value for the generation G after which the tilting force is no longer contained in the error function (10). As a result of this arrangement, at the beginning of the search, when G < G1, the search will find the correct model for the system, and at the end will be for parameter F_mAnd F_tiltGiving a more accurate value.

In practice, when using a genetic algorithm, a period of time during which the mass of the door can be determined sufficiently accurately needs to be combined with the start-up of the system. During the teaching phase, it is assumed that the closing device is in operation and that m is determined after the first door operation_d、F_μDoorAnd F_tiltThe value of (c). This calculation is repeated after a sufficient number of door operations until the calculated door mass value is found to be sufficiently converged. In this teaching stepAfter the section, the system operates in an actual condition monitoring mode in which the mass of the door is assumed to be constant and the parameter CD is not. This operating state is as described above in connection with the description of fig. 4.

For example, we can consider the friction force F when the closure device (CD-0) is excluded from the system_μ. This friction is typically reduced to a somewhat lower level. This is due to the fact that both the movement of the counterweight and the movement of the cable connecting the counterweight to the door are resisted by friction. Thus, when no counterweight is included in the system, the total friction acting on the door is reduced.

In the long-term measurement of the friction force acting on the door, it is possible to monitor the rate of change of the friction force. When the rate of change of the friction force due to wear during normal operation is known, it can be seen whether any abnormally strong wear has occurred or whether there is any other reason to suspect a sudden failure at the moment of observation. From the behaviour of the friction observed during a long time interval (typically a steady increase), it is possible to estimate the point in time at which the risk of failure will exceed a given risk limit.

If the friction force increases in a stepwise manner at a given instant, there is a suspected cause of a serious malfunction regarding the system function. If additional noise is otherwise heard during door movement, a fault condition can be considered to have occurred with nineteen steps. It is also possible to draw conclusions from the way in which the friction values act after a stepwise jump as such. The force may remain constant or may steadily increase or decrease.

When a new automatic door is put into use, its operation starts with a so-called commissioning period, during which the parameters received from the optimizer may vary somewhat as a function of time. The commissioning period is followed by an actual steady operation period during which the parameters of the system (door) are kept practically constant for a long time. On the other hand, during steady operation, the parameter values may also be generally better than during the commissioning period. After a period of steady operation, some loosening of the movable part and some stretching of the easily stretchable part start to occur. For example, rollers that guide the door moving on the track may slip or experience wear until some of the rollers are no longer in contact with the door.

The increase in friction may arise from a number of different causes. Dirt accumulates on the door guide rails and forms an obstacle to the smooth movement of the door on the guide rails. On the other hand, at locations where friction necessitates lubrication, too much lubrication oil may be used and the door does not move in the desired manner. Dirt is particularly prone to build up on the door sill because elevator customers often step on it when entering the elevator car. Motor faults occur naturally, depending on the parameters obtained by the method of the invention. The wear of the cable between the counterweight and the door is also represented by the parameter F_μDoorAn increased value of (a). The pulse-like increase in friction may be due to external mechanical stimuli applied to the door, such as a violent impact occurring when loading an object into the car. A failure in the door pause may also result in a sudden increase in friction. This may also occur as a result of a break in the wire in the cable of the closing weight. If any additional noise is heard from the system in addition to the change in friction, maintenance personnel should be called immediately to the field. If the value of the friction force remains constant after the pulse-like increase of the friction, this should be taken into account in connection with the next scheduled maintenance round of the elevator system, but in this case an immediate action is not necessarily required. Wear of the components contained in an automatic door results in a slow degradation of performance, which may or may not be important for the correct operation of the door.

If the variance (square of the standard deviation) of the increase in friction is detected, it can be concluded that the wear of the door mechanism has improved. The movement of the components increases and the path of the moving parts starts to gradually differ from the new door system with small tolerances. Even when the variance increases, it is possible that the average value of the frictional force remains stable. This situation may also involve an increase in the noise level generated by the motion. The variance may be considered an indicator of the degree of wear.

The season may have an effect on door system parameters obtained in connection with condition monitoring. These changes in conditions may also be reflected in the frictional forces acting on the door if the door is exposed to extreme heat, cold or humidity. The motor may also generate additional heat due to heavy traffic, which results in a reduction of its power. In this case the system interprets this situation as increased friction, but the actual cause is a reduction in the motor power. Similarly, the first door operation in the morning may produce a higher friction value than the system would normally be subjected to, since it is a "cold start" after a pause in operation every night. An example of a variable environmental impact acting on the door on different floors is the difference in air pressure at different floor levels. Depending on the floor on which the door is located, the ventilation system may produce different air flow values with respect to the door.

The basic method for detecting a faulty door is to compare the parameters F for the doors of different floors_tiltAnd F_μDoor. If F of a floor_tiltSignificantly different from the usual range (general line), it can be concluded that the landing door setting angle on the floor is different from the other doors. On the other hand, F significantly deviating from other floors_μDoorThe value may indicate that the adjustment roller of the landing door has been mounted differently from the other doors.

One of the advantages of the invention is that information about the operation of the door can be stored. In this way a database is created covering the operational history of the door, on the basis of which it is possible to plan for example the appropriate date for the next maintenance. From this operation history, the current status of the door operation can be directly inferred, and even the probability of failure at a future point in time and the necessity of performing maintenance can be predicted. From this database it is further possible to deduce what the duration of the commissioning phase is and how long the phase of stable operation of the door is. The effect of the maintenance operation can also be seen from the database.

It is obvious to the person skilled in the art that the invention is not limited to the embodiments described above, in which the invention has been described by way of example, but that different embodiments of the invention are possible within the scope of the inventive concept defined in the claims below.

Claims (14)

1. A method for monitoring the condition of an automatic door in a building, characterized in that the method comprises the steps of:

measuring acceleration or speed of the door and torque of a door motor driving the door;

creating a dynamic model of the door that includes forces acting on the door as part of the model;

modeling acceleration or velocity of the door by using a dynamic model of the door;

calculating an error term as a difference between the measured value and the estimated value of the acceleration or velocity of the door;

calculating a friction force applied to the door by minimizing the error term or a formula derived from and including the error term; and

the operating state of the door is deduced by comparing the calculated friction force and its change with a reference value.

2. The method of claim 1, wherein the method further comprises the steps of:

the acceleration of the door is measured by using an acceleration sensor.

3. A method according to any of the preceding claims 1-2, characterized in that the method further comprises the steps of:

the speed of the door is measured by using a signal proportional to the speed obtained from the motor of the door.

4. A method according to any of the preceding claims 1-2, characterized in that the method further comprises the steps of:

the use parameters are as follows: one or more of the speed of the door, the current of a motor driving the door, the torque coefficient of the motor, the friction couple of the motor, the force factor of a door closing spring, and the mass of a door closing weight as parameters in the dynamic model;

modeling in the model acceleration and velocity of the door as a function of one or more parameters that are the mass of the door, the frictional force applied to the door, and the force resulting from the tilt angle of the door;

calculating a difference between the measured instantaneous door speed and the instantaneous door speed modeled in the model as a first error function;

calculating a second error function by squaring the first error function and summing the squared first error function obtained over a given time period using a desired weighting coefficient;

calculating the parameters by minimizing a second error function: one or more of a door mass, a frictional force applied to the door, and a force caused by a tilt angle of the door; and

the calculated parameters are fed back to the dynamic model for use in the next calculation cycle.

5. A method according to any of the preceding claims 1-2, characterized in that the method further comprises the steps of:

the use parameters are as follows: one or more of acceleration of the door, current of a motor driving the door, torque coefficient of the motor, friction couple of the motor, force factor of a door closing spring, and mass of a door closing weight as parameters in the dynamic model;

modeling in the model the acceleration of the door as a function of one or more parameters, the parameters being the mass of the door, the frictional force applied to the door, and the force resulting from the angle of inclination of the door;

calculating a difference between the measured instantaneous acceleration of the door and the instantaneous acceleration of the door modeled in the model as a third error function;

calculating a fourth error function by squaring the third error function and summing the squared third error function obtained over a given time period using a desired weighting coefficient;

calculating the parameters by minimizing a fourth error function: one or more of a door mass, a frictional force applied to the door, and a force caused by a tilt angle of the door; and

the calculated parameters are fed back to the dynamic model for use in the next calculation cycle.

6. A method according to any of the preceding claims 1-2, characterized in that the method further comprises the steps of:

determining a door mass value in conjunction with activation of the system; and

the mass of the door is defined as a constant value in the dynamic model of the door.

7. A method according to any of the preceding claims 1-2, characterized in that the method further comprises the steps of:

using a genetic algorithm for detecting a failure of the door closing device;

using in the genetic algorithm chromosomes containing genes describing the operation of the closure device, the frictional force applied to the door, and the force caused by the angle of inclination of the door;

using a squared error function as a quality value for the genetic algorithm; and

a dynamic model of the gate is used in determining the phenotype of the genetic algorithm.

8. A system for monitoring the condition of an automatic door in an elevator or building, the system comprising:

at least one door (20, 30, 40) sliding horizontally in its installed position;

a control system (26, 36, 46) for opening and closing the door;

characterized in that the system further comprises:

means (20, 30, 40) for measuring the acceleration or speed of the door and the torque of the motor driving the door;

a dynamic model (22, 32, 42) of the door comprising forces acting on the door;

means (22, 32, 42) for modelling the acceleration or velocity of the door by using a dynamic model of the door;

means (23, 33, 43, 24, 34, 44) for calculating an error term by using information about the measured and modelled acceleration or velocity of the door;

means (25, 35, 45) for calculating the frictional force applied to the door so as to minimize the error term or an expression derived from and containing the error term; and

-means (26, 35, 46) for deducing the operating state of the door for comparing the measured friction and its change with a reference value.

9. The system of claim 8, wherein the system further comprises:

a door control card (26, 36, 46) as a door control system.

10. System according to any of the preceding claims 8-9, characterized in that the system further comprises:

acceleration sensors (30, 40) as means for measuring the acceleration of the door.

11. System according to any of the preceding claims 8-9, characterized in that the system further comprises:

a signal (20) proportional to the speed and obtained from the door motor, which is used as a measure of the speed v of the door_dThe apparatus of (1).

12. System according to any of the preceding claims 8-9, characterized in that the system further comprises:

apparatus for determining one or more parameters in a dynamic model (22) via various operations including the velocity v of a door_dA measurement of the current of the motor driving the door, a determination of the torque coefficient of the motor, a determination of the friction couple of the motor, a determination of the force factor of the door closing spring, and a measurement of the mass of the door closing weight;

means for modeling in the dynamic model (22) the velocity of the door, said velocity being defined as a function of one or more parameters being the mass of the door, the frictional force applied to the door, and the force resulting from the angle of inclination of the door;

-means (23) for calculating a first error function obtained as the difference between the measured instantaneous door speed and the instantaneous door speed modeled in the model;

-means (24) for calculating a second error function by squaring the first error (23) function and summing the squared first error functions obtained over a given time period using a desired weighting coefficient (21);

first optimization means (25) for minimizing a second error function (24) and calculating parameters: one or more of a door mass, a frictional force applied to the door, and a force caused by a tilt angle of the door; and

first feedback for returning the calculated parameters to the dynamic model (22) for use in the next calculation cycle.

13. System according to any of the preceding claims 8-9, characterized in that the system further comprises:

means for determining one or more parameters in the dynamic model (32) via various operations including measurement of door acceleration, measurement of motor current driving the door, determination of torque coefficient of the motor, determination of friction couple of the motor, determination of force factor of the door closing spring, and measurement of door closing weight mass;

means for modeling in the dynamic model (32) the acceleration of the door, said acceleration being defined as a function of one or more parameters being the mass of the door, the frictional force applied to the door, and the force caused by the angle of inclination of the door;

-means (33) for calculating a third error function obtained as the difference between the measured instantaneous acceleration of the door and the instantaneous acceleration of the door modeled in the model;

-means (34) for calculating a fourth error function by squaring the third error function (33) and summing the squared third error functions obtained over a given time period using a desired weighting coefficient (31);

second optimization means (35) for minimizing a fourth error function (34) and calculating parameters: one or more of a door mass, a frictional force applied to the door, and a force caused by a tilt angle of the door; and

second feedback for returning the calculated parameters to the dynamic model (32) for use in the next calculation cycle.

14. System according to any of the preceding claims 8-9, characterized in that the system further comprises:

-third optimization means (45) for detecting a malfunction of the door closing device using a genetic algorithm;

the third optimizing means (45) described above for using one or more parameters in the genetic algorithm, which are the operation of the closing device, the frictional force applied to the door, and the force caused by the inclination angle of the door, as genes of the chromosome;

-said third optimizing means (45) for using a squared error function (44) as the quality value of the genetic algorithm; and

-said third optimization means (45) for using a dynamic model (42) of the gate in determining the phenotype of the genetic algorithm.