JP2006069699A - Method and device for diagnosing elevator system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method for diagnosing an elevator system for exactly diagnosing the state of the elevator system. <P>SOLUTION: In this method, (a) a plurality of measured values are acquired during a stipulated car operation to be made as comparison reference time series data, (b) a plurality of measured values are acquired for a time longer than a time required for the stipulated car operation to be made as original time series data at the time of diagnosing the elevator system, (c) comparison object time series data having a maximum correlation function with the comparison reference time series data are extracted from the original time series data, (d) an absolute value of a difference of a measured value of the comparison reference time series data and a measured value of the comparison object time series data corresponding to the measured value of the comparison reference time series data is calculated for each measured value, and absolute values of each measured value are totalized to be made as a total value, (e) the total value is made as a relation value for showing the relation of the reference time series data and the comparison object time series data, and (f) the state of the elevator system is decided based on the relation value. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to an elevator system diagnosis method for finding an abnormality in an elevator system and an elevator system diagnosis apparatus using the method.

The elevator system diagnosis performed by the elevator system diagnosis apparatus performs frequency analysis on time-series data consisting of a plurality of measurement values (for example, acceleration and noise measurement values) indicating the state of the elevator system that fluctuates depending on the operation of the car. Compared with the frequency analysis result of the time series data at the normal time, the abnormality or normality is determined. For example, the elevator system diagnosis apparatus disclosed in Patent Document 1 measures vibration generated by running of a car with an acceleration sensor, performs frequency analysis on time-series data including a plurality of measurement values, and analyzes the frequency by using frequency analysis. By comparing the frequency characteristics of the elevator system with the frequency characteristics of the elevator system when the elevator system is normal, the presence / absence, abnormality location, and degree of abnormality (level) of the elevator system are determined.
JP 09-2752 A

  Of course, to compare the time series data obtained when the elevator system is normal (normal time series data) with the time series data obtained when the elevator system is diagnosed, the operation of the car to obtain both time series data ( Hereinafter, the “regular car operation” is referred to as “the operation of the specified car”, for example, the operation completion time, speed, acceleration, and the like must be the same. At this time, the elevator system diagnosis apparatus notifies the start time of the specified car operation, in other words, the start time of acquisition of the time series data from the elevator system control apparatus, and accurately acquires the time series data at the notified time. Must start. Therefore, a high-accuracy and high-speed communication means is required so as not to cause a time lag in communication between the elevator system diagnostic apparatus and the elevator system control apparatus. In addition, each of the elevator system diagnostic device and the elevator system control device has a clock, and it is conceivable to start the acquisition of time series data and the start of a specified car operation by matching the time. There is no guarantee that the start time is the same.

  Therefore, the present invention provides time-series data acquired when the elevator system is normal, even if the start time of the time-series data acquisition of the elevator system diagnostic device does not exactly match the start time of the car operation specified by the elevator system control device. Provides an elevator system diagnostic method for accurately diagnosing the state of an elevator system from a highly accurate comparison result, and an elevator system diagnostic device using the same This is the issue.

The elevator system diagnosis method of the present invention is an elevator system that determines the state of the elevator system by comparing a plurality of time-series data composed of a plurality of measurement values indicating the state of the elevator system that fluctuates according to the operation of the car. A diagnostic method,
a) acquiring a plurality of measured values during the operation of the specified car, and making the comparison reference time-series data;
b) acquiring a plurality of measured values during a time longer than the time required for the specified car operation when the specified car operation is performed at the time of diagnosis of the elevator system, and making the original time series data; ,
c) extracting from the original time series data the comparison target time series data that maximizes the correlation function with the comparison reference time series data;
d) calculating the absolute value of the difference between the measured value of the comparative reference time series data and the measured value of the comparative time series data corresponding to the measured value of the comparative reference time series data for each measured value; A process of aggregating the absolute values of each measured value to make a total value,
e) setting the total value as a relation value indicating a relation between the reference time series data and the comparison time series data;
and f) determining a state of the elevator system based on the relationship value.

  According to the present invention, the time series data acquired when the elevator system is normal, even if the start time of the time series data acquisition of the elevator system diagnosis apparatus and the start time of the car operation specified by the elevator system control apparatus do not match exactly, Time series data acquired at the time of abnormality diagnosis can be compared with high accuracy, and the state of the elevator system can be accurately diagnosed from a high accuracy comparison result.

  The elevator system diagnosis apparatus according to the present invention is used when an automatic diagnosis of an elevator system is performed. Briefly, the automatic diagnosis performs the specified car operation after confirming that there are no people in the car at a time when the usage rate of the elevator system is low, for example, at night when there are few users. At the same time, a physical quantity (for example, noise, temperature, etc.) or a state quantity (for example, speed, acceleration, etc.) indicating the state of the elevator system, which fluctuates according to a specified car operation, is measured using a sensor. The state of the elevator system is diagnosed from this measurement result. In the diagnosis, the normality or abnormality of the elevator system at the time of diagnosis is determined by comparing with the measurement result measured when the elevator system is normal.

  Here, the specified car operation is an operation of the car that is performed in a specified time, and specifically speaking, a driving operation between specific floors, a car door opening and closing operation, a driving operation that repeatedly accelerates and decelerates, etc. An operation in which the entire operation that can be performed by the car is performed in a specified time.

  The elevator system diagnosis apparatus of the present invention is notified of the start time of the specified car operation for automatic diagnosis from the elevator system control device, or the specified car operation at the time when the predetermined specified car operation is started. Measurement of the physical quantity or state quantity indicating the state of the elevator system that fluctuates depending on the time series, and acquisition of time series data is started. However, as will be described later, it is not necessary to accurately match the time when the time series data acquisition starts with the start time of the specified car operation, for example, before the start time of the specified car operation. It is determined one second before the start time.

  Alternatively, the elevator system diagnosis apparatus may instruct the elevator system control apparatus to perform a specified car operation after starting the acquisition of time series data.

  Hereinafter, an elevator system diagnostic apparatus will be described. An elevator system diagnosis apparatus according to an embodiment of the present invention diagnoses an elevator system using time-series data including a plurality of measurement values obtained by measuring acceleration of a moving car.

Embodiment 1 FIG.
FIG. 1 schematically shows an elevator system diagnostic apparatus 12 according to the present invention provided in an elevator system 10. The elevator system diagnostic device 12 is mounted on the car 14 of the elevator system 10 and measures the acceleration of the car 14 traveling in the direction of the arrow 20 in the elevator shaft 18 by the acceleration sensor 16 attached to the car 14. is doing. Further, the elevator system diagnosis device 12 notifies the elevator system administrator outside the elevator system 10 of the result of the elevator system diagnosis through communication means 22 such as a wireless LAN or a telephone line.

  FIG. 2 schematically shows the configuration of the elevator system diagnostic apparatus 12. The elevator system diagnosis apparatus 12 stores time-series data acquisition unit 30 that acquires time-series data indicating the fluctuation of the acceleration of the car 14 from the acceleration sensor 16 and time-series data acquired by the time-series data acquisition unit 30. A time-series data storage unit 32; a comparison-target time-series data extraction unit 34 that extracts comparison-target time-series data Ay to be compared with the comparison-reference time-series data Ax from the original time-series data Ao; The relation value calculation unit 36 that calculates the relation value by comparing the comparison target time series data Ay, the state of the elevator system when the comparison reference time series data Ax is acquired based on the relation value, and the comparison target time series data A determination result output unit 38 that outputs a comparison determination result with the state of the elevator system when Ay is acquired, for example, to an elevator system administrator; A.

  Hereinafter, details of the components of the elevator system diagnosis apparatus 12 will be described together with an elevator system diagnosis procedure.

  The acceleration sensor 16 continuously measures the acceleration of the moving car 14 and outputs the measurement result to the time-series data acquisition unit 30 as a time-series waveform of the voltage signal. FIG. 3 shows a time-series waveform of the voltage signal output from the acceleration sensor 16 when the elevator system 10 is normal. This is because the car 14 keeps accelerating for a certain period of time from the stop state, then maintaining at a constant speed for a certain period of time, and subsequently decelerating and stopping at a certain period of time for a specified time of 9 seconds. This is a measurement of the cage behavior specified to be performed. Hereinafter, in explaining the elevator system diagnostic apparatus, this is assumed to be a specified car operation.

  In FIG. 3, the car is accelerating when the voltage signal is positive, and the car is decelerating when the voltage signal is negative. A voltage signal of 0 indicates that the car is not accelerating or decelerating.

The time-series data acquisition unit 30 performs analog-to-digital conversion on the time-series waveform of the voltage signal indicating the acceleration variation output from the acceleration sensor 16 to create and acquire time-series data A indicating the acceleration variation. For example, the time series data A is represented by a set of l voltage values (a plurality of measured values) as shown in Equation 1. The number (l) of the plurality of measurement values included in the time series data A is determined by the time required for completing the specified car operation (for example, the above-mentioned 9 seconds) and the resolution of analog-digital conversion.

  The time series data acquisition unit 30 outputs the acquired time series data to the time series data storage unit 32.

  The time series data storage unit 32 stores a plurality of time series data. As a plurality of time-series data, comparison reference time-series data Ax, which is a reference for comparison when determining whether the elevator system is normal or abnormal, is stored during normal car operation when the elevator system is normal. In addition, original time series data Ao acquired at the time of a specified car operation at the time of elevator system diagnosis, which is a source of extraction of time series data (comparison target time series data) to be compared with comparison reference time series data Ax, is saved. Has been. Details of the comparison reference time series data Ax and the original time series data Ao will be described later.

The comparison target time series data extraction unit 34 extracts the comparison target time series data Ay to be compared with the comparison reference time series data Ax from the original time series data Ao stored in the time series data storage unit 32. The comparison reference time series data Ax indicates a change in the acceleration of a specified car operation performed when the elevator system is normal, and is a set having m measurement values as constituent elements as shown in Equation 2.

The original time series data Ao indicates a change in acceleration measured by the acceleration sensor 16 during a time longer than the time required for completing the specified car operation when the specified car operation is performed at the time of diagnosis of the elevator system 10. As shown in Equation 3, the set includes n measurement values larger than m as constituent elements.

The comparison target time series data Ay is composed of a set of m consecutive constituent elements that are included in the original time series data Ao and have the same number of measurement values as the comparison reference time series data Ax. For example, when the original time series data Ao is composed of m + 2 measurement values (when n = m + 2), the comparison target time series data Ay is [Ao1, Ao2, Ao3, ..., Aom], [Ao2]. , Ao3, Ao4, ..., Aom + 1] and [Ao3, Ao4, Ao5, ..., Aom + 2]. Among these, a set having a maximum correlation function with the comparison reference time series data Ax is extracted as comparison target time series data Ay by the comparison target time series data extraction unit 34. The comparison target time series data Ay is expressed as shown in Equation 4, for example. In this way, the comparison target time series data Ay is extracted from the original time series data Ao so that the correlation function with the comparison reference time series data Ax is maximized.

  The comparison target time series data extraction unit 34 outputs the extracted comparison target time series data Ay to the determination value calculation unit 36.

The relationship value calculating unit 36 measures the measurement values (Ax1, Ax2, Ax3,..., Axm) of the comparison reference time series data Ax, and the measurement values ((Ay1 , Ay2, Ay3,..., Aym) are calculated for each measured value, and the total of the absolute values of the respective measured values is calculated as comparison reference time series data Ax and comparison target time series data Ay. Assuming that a plurality of measured values of the comparison reference time series data Ax are Axi (i is an integer of 1 to m) and a measured value of the comparison time series data Ay is Ayi, the relation value d1 is expressed by the equation shown in Formula 5.

  Also, a relation value calculated by another method can be used. FIG. 4 is a diagram for facilitating understanding of the concept used to obtain the relation value d2 by a method different from that described above. The horizontal axis represents the measured value Axi (i is an integer of 1 to m) of the comparison reference time series data Ax, and the vertical axis represents the measured value Ayi of the comparison time series data Ay. FIG. 4A is a diagram showing a relationship between the comparison target time series data Ay acquired when the elevator system is in the state 1 and the comparison reference time series data Ax acquired when the elevator system is normal. On the other hand, FIG. 4B shows the relationship between the comparison target time series data Ay acquired when the elevator system is in the state 2 and the comparison reference time series data Ax acquired when the elevator system is normal. FIG. In these figures, the measured values Axi and Ayi are plotted as points (Axi, Ayi).

When the elevator system is normal when the elevator system is diagnosed, the measured value Ayi of the comparison target time series data Ay is equal to the measured value Axi of the corresponding comparison reference time series data Ax measured when the elevator system is normal. That is, the point (Axi, Ayi) is plotted on the straight line of Ayi = Axi shown in the figure. On the other hand, at the time of elevator system diagnosis, if the elevator system is not normal (abnormal), the point (Axi, Ayi) is plotted away from the straight line of Ayi = Axi. The distances from the point (Axi, Ayi) to the straight line of Ayi = Axi are totaled to obtain the relation value d2. The distance Lj between the straight line Ayi = Axi from an arbitrary point (Axj, Ayj) (j is an integer not less than 1 and not more than m) is obtained by the equation (6), and the relation value d2 obtained by tabulating these distances Lj is: It is obtained by the equation of Equation 7.

  The relationship value calculation unit 36 outputs the calculated relationship value to the determination result output unit 38.

  The determination result output unit 38 determines whether the state of the elevator system at the time of elevator system diagnosis is normal or abnormal based on the relationship value calculated by the determination value calculation unit 36 and outputs the determined value. The determination result output unit 38 determines whether the elevator system is normal or abnormal depending on whether or not the relationship value exceeds a prescribed threshold value. The predetermined threshold value is determined to be a value at which a change in the state of the elevator system can be detected at a minimum. That is, when the threshold setting is excessively strict, specifically, only when the measured values (time-series data) measured at the time of diagnosis of the elevator system and when the elevator system is normal substantially match (the determination value is substantially 0). If the system status is normal, for example, the elevator system is determined to be abnormal due to factors that are not related to the status of the elevator system, such as the surrounding environment such as the allowable measurement error of the sensor and the temperature at the time of measurement. The threshold value is set to a value that takes this into consideration.

  The determination result output unit 38 notifies the elevator system administrator of the determination result of whether the elevator system is normal or abnormal by the communication means 22.

  According to such a configuration, when the time series data acquisition start time of the elevator system diagnosis device and the car operation start time specified by the elevator system control device do not exactly match, the time is acquired when the elevator system is normal. The series data and the time series data acquired at the time of abnormality diagnosis can be compared with high accuracy, and the state of the elevator system can be accurately diagnosed from the high accuracy comparison result.

Embodiment 2. FIG.
When there is an abnormality in the elevator system, the elevator system diagnosis apparatus of the present embodiment specifies the type of abnormality.

  FIG. 5 schematically shows the configuration of the elevator system diagnostic apparatus 112 according to the present embodiment. The elevator system diagnosis apparatus 112 includes an acceleration sensor 116 having substantially the same function as that of the first embodiment, a time series data acquisition unit 130, a time series data storage unit 132, a comparison target time series data extraction unit 134, and a relation value calculation unit 136. Have The description about these is simplified.

In addition to these, the elevator system diagnosis apparatus 112 divides the time series data into a plurality of time series sections, a time series waveform decomposition unit 140 that decomposes the time series waveform output from the acceleration sensor 116 for each of a plurality of frequency bands. A time series data dividing unit 142, a matrix data creating unit 144 that creates matrix data including a plurality of relation values, a matrix data saving unit 146 that saves matrix data, and a determination result output unit 148.

  Hereinafter, details of the components of the elevator system diagnosis apparatus 112 will be described together with an elevator system diagnosis procedure.

  Since the acceleration sensor 116 is the same as the acceleration sensor 16 of Embodiment 1, description is abbreviate | omitted.

  The time series waveform decomposition unit 140 decomposes the time series waveform of the voltage signal output from the acceleration sensor 116 for each of a plurality of frequency bands. For example, the time series waveform of the voltage signal output from the acceleration sensor 116 shown in FIG. 3 is decomposed into time series waveforms for a plurality of frequency bands 1 to 7 as shown in FIG. The frequency band 1 is the lowest frequency, the frequency increases in order, and the frequency band 7 is the highest frequency. The frequency value of the frequency band and the number of frequency bands are arbitrarily set.

  The time-series data acquisition unit 130 creates and acquires time-series data for each of a plurality of frequency bands according to the same procedure as the time-series data acquisition unit 30 of the first embodiment.

  The time series data storage unit 132 stores time series data for each of a plurality of frequency bands. A plurality of original time series data Ao1 to Ao7 for each frequency band and a plurality of comparison time series data Ax1 to Ax7 for each frequency band are stored.

  The comparison target time-series data extraction unit 134 follows the same procedure as the comparison target time-series data extraction unit 34 of the first embodiment, and compares a plurality of frequency band-based original time-series data Ao1 to Ao7 with a plurality of frequency-band comparison targets. Time series data Ay1 to Ay7 are extracted.

  The time-series data dividing unit 142 divides the comparison reference time-series data Ax1 to 7 for each frequency band and the comparison-target time-series data Ay1 to Ay7 for each frequency band into a plurality of time-series sections. For example, as shown in FIG. 6, a specified car operation (the car keeps accelerating for a certain period of time from a stopped state, then maintained at a constant speed for a certain period of time, and then decelerated and stopped for a certain period of time. The car operation in which the operation is performed for a predetermined time of 9 seconds) is divided into five time-series sections. In section 1, the operation of the car accelerating from the stop, in section 2, the operation of the car shifting from constant acceleration to constant speed, in section 3, the operation of moving the car at constant speed, section 4 In, the operation of the car decelerating from a constant speed is performed, and in the section 5, the operation of stopping the car is performed.

  Therefore, the time-series data output from the time-series data dividing unit 142 is, for example, the comparison reference time-series data Ax is represented by 35 time-series data, here, the number of frequency bands × the number of time-series sections. Hereinafter, in the time series section l of the frequency band k, the comparison reference time series data is expressed as Axkl, and the comparison target time series data is expressed as Aykl. Here, k is an integer of 1 to 7, and l is an integer of 1 to 5.

  The relationship value calculation unit 136 shows the relationship between the comparison reference time series data Axkl and the comparison target time series data Aykl of the same frequency band and the same time series section according to the same procedure as the relationship value calculation unit 36 of the first embodiment. Calculate the relationship value. Hereinafter, the relation value of the time series section 1 in the frequency band k is expressed as dkl. The relationship value calculation unit 136 calculates relationship values in all time series sections in all frequency bands.

The matrix data creation unit 144 creates relationship value matrix data from the relationship values in all time series sections in all frequency bands calculated by the relationship value calculation unit 136. The relationship value matrix data M has a form as shown in Equation 8, for example.

  In the matrix data M shown in Equation 8, d11 is a relation value indicating the relationship between the comparison reference time series data Ax11 and the comparison target time series data Ay11 in the time series section 1 of the frequency band 1. Dkl is a relational value indicating the relationship between the comparison reference time-series data Axkl and the comparison-target time-series data Aykl in the time-series section l of the frequency band k.

  The matrix data creation unit 144 stores the created matrix data M in the matrix data storage unit 146 and outputs it to the determination result output unit 148.

  The matrix data storage unit 146 has a plurality of matrix data, and stores the matrix data created by the matrix data creation unit 144. As the accumulated matrix data, there are abnormal matrix data Ma1 and Ma2 to which information on abnormal contents of the elevator system when the comparison target time series data Ay related to the relational values constituting the matrix data is acquired exists. To do. The abnormality matrix data Ma will be described later.

  The determination result output unit 148 determines the matrix data M output from the matrix data creation unit 144, thereby obtaining the state of the elevator system when the comparison target time series data related to the relational values constituting the matrix data is acquired. Determine normal or abnormal. The determination result output unit 148 compares the abnormal matrix data Ma stored in the matrix data storage unit 146 with the matrix data M output from the matrix data creation unit 144, and determines the match or similarity between them.

  If they match or are similar, the state of the elevator system when the matrix data M output from the matrix data creation unit 144 is created is determined to be an abnormality in the elevator system when the abnormality matrix data Ma is created.

  Further, if they do not match or are not similar, it is determined that a new abnormality has occurred in the elevator system when the matrix data M is created. In addition, when a plurality of relational values constituting the matrix data M output from the matrix data creation unit 144 are within 0 or a prescribed threshold value, it is determined that the elevator system when the matrix data M is created is normal. . The prescribed threshold value is determined to be a value that is not determined to be abnormal by an element that is not related to the state of the elevator system, as in the first embodiment.

  If the matrix data M output from the matrix data creation unit 144 indicates an abnormality in the new elevator system, after the cause of the abnormality is investigated, information on the content of the abnormality cause is stored in the matrix data storage unit 146. It is given to the accumulated matrix data M. As a result, the matrix data becomes abnormal matrix data indicating a new abnormality, and is used for comparison with new matrix data thereafter.

  In the present embodiment, the time series data is decomposed for each frequency band and divided for each time series section, but either one may be used.

  According to the present embodiment, it is possible to specify the type of abnormality in the elevator system by using the time series data divided for each frequency band and for each time series. Further, by accumulating abnormal matrix data indicating abnormalities in the state of the elevator system, the abnormalities in the elevator system when the matrix data that matches or is similar to the abnormal matrix data are matched or similar to the past abnormalities. As a result, it is possible to easily identify the cause of the abnormality of the elevator system, and to recover from the abnormality in a short time.

  Although the elevator system diagnostic apparatus according to the two embodiments has been described above, it is obvious that those skilled in the art can easily make modifications of these elevator system diagnostic apparatuses. For example, the speed may be measured as a state quantity indicating the state of the elevator system, or noise indicating a physical quantity may be measured. Elevator systems can be diagnosed with speed and noise as well as acceleration.

  Further, the comparison reference time series data Ax may not be acquired when the elevator system is normal. The comparison control time series data Ay acquired in the past may be used. For example, the time series data acquired immediately before is set as the comparison reference time series data Ax, and the currently acquired time series data is set as the comparison time series data Ay. Then, it is possible to compare and determine the state of the previous elevator system and the current state of the elevator system.

It is the schematic of the elevator system in which the elevator system diagnostic apparatus was installed. 1 is a schematic configuration diagram of an elevator system according to a first embodiment. It is a figure which shows the time-sequential waveform of the voltage signal which an acceleration sensor outputs. It is a figure which shows the concept of the calculation method of a relationship value. It is a schematic block diagram of the elevator system of Embodiment 2. It is a figure which shows the time series waveform which decomposed | disassembled the time series waveform shown in FIG. 3 for every frequency band, and was divided | segmented for every time series area.

Explanation of symbols

10 elevator system, 12 elevator system diagnostic device, 14 car, 16 acceleration sensor, 18 shaft, 20 direction, 22 communication means,
30 time-series data acquisition unit, 32 time-series data storage unit, 34 comparison target time-series data extraction unit, 36 relation value calculation unit, 38 determination result output unit,
Ao original time series data, Ax comparison reference time series data, Ay comparison control time series data,
112 Elevator System Diagnosis Device, 116 Acceleration Sensor, 130 Time Series Data Acquisition Unit, 132 Time Series Data Storage Unit, 134 Comparison Time Series Data Extraction Unit, 136 Relation Value Calculation Unit, 140 Time Series Waveform Decomposition Unit, 142 Time Series Decomposition , 144 matrix data creation unit, 146 matrix data storage unit, 148 judgment result output unit,
Aok Original time series data by frequency band, Axk Reference time series data by frequency band M1 Abnormal matrix data, M2 Abnormal matrix data

Claims (9)

An elevator system diagnosis method for determining a state of the elevator system by comparing a plurality of time-series data composed of a plurality of measurement values indicating a state of the elevator system that varies depending on the operation of the car,
a) acquiring a plurality of measured values during the operation of the specified car, and making the comparison reference time-series data;
b) At the time of diagnosis of the elevator system, a plurality of measured values are acquired during a time longer than the time required for the specified car operation in which the one specified car operation is performed, and used as original time series data Process,
c) extracting from the original time series data the comparison target time series data that maximizes the correlation function with the comparison reference time series data;
d) calculating the absolute value of the difference between the measured value of the comparative reference time series data and the measured value of the comparative time series data corresponding to the measured value of the comparative reference time series data for each measured value; A process of aggregating the absolute values of each measured value to make a total value,
e) setting the total value as a relation value indicating a relation between the reference time series data and the comparison time series data;
and f) determining the state of the elevator system based on the relationship value. The plurality of measurement values are a plurality of measurement values measured for a plurality of frequency bands,
The elevator diagnosis method according to claim 1, wherein the step f) determines the state of the elevator system based on a relation value for each of the plurality of frequency bands. The plurality of measurement values are a plurality of measurement values measured for each of a plurality of time series sections that constitute the time required for the car operation specified above,
3. The elevator diagnosis method according to claim 1, wherein the step f) determines the state of the elevator system based on the relationship value for each of the plurality of time-series sections.   The elevator diagnostic method according to any one of claims 1 to 3, wherein the comparison reference time series data is acquired when the elevator system is normal. An elevator system diagnostic apparatus for determining the state of the elevator system by comparing a plurality of time-series data consisting of a plurality of measured values indicating the state of the elevator system that varies depending on the operation of the car,
A sensor for measuring the state of the elevator system;
A time-series data acquisition unit that acquires measurement results output from the sensor as time-series data including a plurality of measurement values;
A time-series data storage unit that stores the time-series data and stores comparison reference time-series data acquired during a specified car operation;
Correlation with the comparative reference time-series data from the original time-series data acquired during a time longer than the time required for the specified car operation when the specified car operation is performed at the time of diagnosis of the elevator system A comparison time series data extraction unit that extracts comparison time series data that maximizes the function;
The absolute value of the difference between the measured value of the comparative reference time-series data and the measured value of the comparative time-series data corresponding to the measured value of the comparative reference time-series data is calculated for each measured value. A relationship value calculation unit that sets a total value of absolute values of measurement values as a relationship value indicating a relationship between the reference time series data and the comparison time series data;
And a determination result output unit for determining the state of the elevator system based on the relationship value. A time series waveform decomposition unit that decomposes the time series waveform of the measurement result output from the sensor into a time series waveform for each of a plurality of frequency bands;
The time series data acquisition unit acquires the time series data for each of the plurality of frequency bands from the time series waveform for each of the plurality of frequency bands,
The elevator system diagnosis apparatus according to claim 5, wherein the determination result output unit determines a state of the elevator system based on a relation value for each of the plurality of frequency bands. A time-series data dividing unit that divides the comparison reference time-series data and the comparison target time-series data into a plurality of time-series sections constituting a time required for the specified car operation;
The said determination result output part determines the state of the said elevator system based on the relational value for every said time series area, The elevator system diagnostic apparatus as described in any one of Claim 5 or 6 characterized by the above-mentioned. A matrix data creating unit that creates matrix data composed of at least one relationship value for each of the plurality of frequency bands or for each of the plurality of time-series sections;
A matrix data storage unit for storing the matrix data;
The determination result output unit compares the new matrix data created by the matrix data creation unit with the past matrix data created by the matrix data creation unit stored in the matrix data storage unit, and The elevator system diagnosis apparatus according to claim 6, wherein a state of the elevator system is determined. 9. The elevator system diagnosis apparatus according to claim 8, wherein information on abnormal contents of the elevator system is added to the matrix data stored in the matrix data storage unit.

Images