JP4876136B2 - Abnormality diagnosis apparatus and abnormality diagnosis method - Google Patents

Description

  The present invention relates to an abnormality diagnosis device and an abnormality diagnosis method for detecting a physical signal generated according to an operating state of a moving body such as an elevator and analyzing the detected physical signal by a predetermined calculation to make a diagnosis.

  For example, the operation sound of an elevator to be diagnosed is collected by an acoustic sensor such as a microphone, and the acoustic signal generated by the acoustic sensor is collected in advance and compared with a registered acoustic signal during normal operation. Therefore, there is known an abnormality diagnosis method for detecting normal operation and abnormal operation of the elevator.

  In order to perform abnormality diagnosis using the above-described method, it is necessary to be careful not to allow ambient environmental sounds such as human speech to enter when recording the operation sound of the elevator. The reason is that the elevator to be diagnosed is sound and does not generate abnormal noise during operation, but erroneously diagnoses the environmental sound component mixed in the acoustic signal as abnormal noise of the elevator This is because it is possible.

  In order to solve this problem, for example, Patent Document 1 discloses that a voice of a person is mixed in an acoustic signal to be diagnosed by recording an operation sound of an elevator during a time period when there is no person and diagnosing the abnormality. An abnormality diagnosis apparatus is disclosed which is to be prevented.

JP 2004-277082 A

  The abnormality diagnosis device disclosed in Patent Document 1 is considered to be effective for elevators installed in an environment in which a time zone in which no people are present can be known in advance, such as late at night in department stores and railway stations. It is done. However, it is often difficult to specify a time zone where no person is present, such as an apartment house or a multi-purpose building, where the elevator is installed. In addition to human voices, there are various environmental sounds such as sounds generated by traffic vehicles, busy streets in busy streets, howling dogs and so on. Therefore, as in the abnormality diagnosis device disclosed in Patent Document 1, it is not possible to prevent the mixing of various environmental sounds simply by setting a time zone when no people are present and performing the abnormal diagnosis. It was the cause of misdiagnosis.

  The present invention has been made to solve the above-described problem, and is capable of preventing an environmental sound that causes a misdiagnosis from occurring and improving the reliability of a diagnosis result and an abnormality diagnosis method. The purpose is to provide.

In order to solve the above-mentioned problem, the abnormality diagnosis device for a moving body according to the present invention detects a physical signal generated according to the operating state of the moving body, analyzes the detected physical signal by a predetermined calculation, and diagnoses it. An abnormality diagnosis device to perform, (1) a storage unit storing physical signals in a reference diagnosis environment, and (2-1) a state in which the moving body stops operation when there is a diagnosis request (2-2) The obtained physical signal is converted into a frequency component to perform cluster analysis, and one or more belonging to the first group obtained by the cluster analysis is acquired. Is obtained by calculating the radius and center coordinates of each cluster, converting the physical signal stored in the storage unit into frequency components, and performing cluster analysis. Calculate radius and center coordinates of each of one or more clusters belonging to the second group, and (2-3) each radius and center coordinates of the clusters belonging to the first group and belong to the second group By comparing the respective radii and center coordinates of the clusters, it is identified whether or not the cluster belonging to the first group is the same as the cluster belonging to the second group, and (2-4) the identification As a result, when the cluster belonging to the first group is identified as the same as the cluster belonging to the second group, it is determined that the moving body is in an diagnosable environment, and the moving body is put into an operating state. It is, to acquire a physical signal generated in accordance with the operating condition of its analyzes based on the predetermined operation, this having a, an arithmetic processing unit for performing an abnormality diagnosis of the movable body The features.

  According to the present invention, it is possible to provide an abnormality diagnosing device and an abnormality diagnosing method that can prevent the mixing of environmental sounds that cause misdiagnosis and improve the reliability of diagnosis results.

1 is a block diagram showing a configuration of an abnormality diagnosis apparatus according to a first embodiment of the present invention. The flowchart which showed the process sequence of the diagnosis request | requirement production | generation part in the abnormality diagnosis apparatus which concerns on the 1st Embodiment of this invention. The flowchart which showed the process sequence of the driving | running state determination part in the abnormality diagnosis apparatus which concerns on the 1st Embodiment of this invention. The flowchart which showed the process sequence of the measurement environment difference calculation part in the abnormality diagnosis apparatus which concerns on the 1st Embodiment of this invention. The block chart which showed the flow of the signal processing for the measurement environment difference calculation by the measurement environment difference calculation part in the abnormality diagnosis apparatus which concerns on the 1st Embodiment of this invention. Explanatory drawing for demonstrating the outline | summary of the cluster analysis by the measurement environment difference calculation part in the mobile diagnostic apparatus which concerns on the 1st Embodiment of this invention. The figure which shows an example of the data structure of the cluster information table in the abnormality diagnosis apparatus which concerns on the 1st Embodiment of this invention. The flowchart which showed the cluster identification processing procedure of the measurement environment difference calculation part in the abnormality diagnosis apparatus which concerns on the 1st Embodiment of this invention. The figure which showed an example of the data structure of the check table in the abnormality diagnosis apparatus which concerns on the 1st Embodiment of this invention. The flowchart which showed the process sequence of the diagnosis availability determination part in the abnormality diagnosis apparatus which concerns on the 1st Embodiment of this invention. The flowchart which showed the process sequence of the diagnostic process part in the abnormality diagnosis apparatus which concerns on the 1st Embodiment of this invention. The flowchart which showed the process sequence of the measurement environment difference calculation part in the abnormality diagnosis apparatus which concerns on the 2nd Embodiment of this invention. The block chart which showed the flow of the signal processing for the measurement environment difference calculation by the measurement environment difference calculation part in the abnormality diagnosis apparatus which concerns on the 2nd Embodiment of this invention The figure which showed an example of the data structure of the measurement environment difference table in the abnormality diagnosis apparatus which concerns on the 2nd Embodiment of this invention. The flowchart which showed the process sequence of the diagnosis availability determination part in the abnormality diagnosis apparatus which concerns on the 2nd Embodiment of this invention.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

<First Embodiment>
(Configuration of abnormality diagnosis device)
FIG. 1 is a block diagram showing the configuration of the abnormality diagnosis apparatus according to the first embodiment of the present invention. The abnormality diagnosis apparatus 2 according to the first embodiment of the present invention is configured to be connected to an elevator 1 to be diagnosed.

  The elevator 1 includes an elevator controller 11 that controls the raising / lowering operation of the cage, and a sensor 12 that collects an acoustic signal generated by the operation of the cage. Here, the sensor 12 includes a load sensor that is installed in an arbitrary place of the cage and measures the weight of the cage, in addition to an acoustic sensor that acquires physical signals such as environmental sounds.

  The abnormality diagnosis apparatus 2 includes a storage unit 20 in which a storage medium such as an IC (Integrated Circuits) memory and a hard disk device is mounted, and a calculation in which a CPU (Central Processing Unit) that executes a program stored in the storage unit 20 is mounted. A processing unit 21. Here, in addition to the program, the storage unit 20 stores preset values such as threshold values, and various data created by the program (a cluster information table 201, a check table 202, a measurement environment difference table 203 and the like described later). Retained.

  Processing functions such as the diagnosis request generation unit 211, the operation state determination unit 212, the measurement environment difference calculation unit 213, the diagnosis availability determination unit 214, and the diagnosis processing unit 215 in the arithmetic processing unit 21 are stored in the storage unit 20. This is realized by executing a predetermined program.

  In FIG. 1, the diagnosis request generation unit 211 has a function of monitoring time, generating a diagnosis request signal for requesting execution of abnormality diagnosis at a preset time, and starting the operation state determination unit 212. The operation state determination unit 212 obtains the cage operation state information and the cage weight information from the elevator control device 11, and monitors the fact that the cage is stopped and no operation noise is generated for diagnosis. It has a function of determining whether or not the elevator 1 as a target is in a state in which abnormality diagnosis is possible. The measurement environment difference calculation unit 213 collects environmental sounds (acoustic signals) generated around the cage via the sensor 12, and the environmental sounds collected here and the reference stored in the storage unit 20 in advance. It has a function of comparing the sound data and calculating the difference, and determining whether or not the elevator 1 is in an environment where diagnosis is possible based on the difference. The diagnosis availability determination unit 214 has a function of evaluating the difference evaluated by the measurement environment difference calculation unit 213 and determining whether or not the abnormality diagnosis of the elevator 1 can be performed. The diagnosis processing unit 215 has a function of performing abnormality diagnosis by converting acoustic signals collected from the sensor 12 included in the elevator 1 into frequency components (Fourier transform) and comparing the magnitude of each frequency component with a reference value.

(Operation of abnormality diagnosis device)
The operation of the abnormality diagnosis device 2 according to the first embodiment of the present invention shown in FIG. 1 will be described below with reference to FIGS.

  FIG. 2 is a flowchart illustrating a processing procedure of the diagnosis request generation unit 211 in the abnormality diagnosis apparatus 2. The function of the diagnosis request generation unit 211 is to generate a diagnosis request signal and activate the operation state determination unit 212 at a preset time, and the diagnosis request generation unit 211 is repeatedly executed at a constant cycle time. The

  That is, the diagnosis request generation unit 211 illustrated in FIG. 2 is activated at regular intervals (for example, every 5 minutes), and acquires the current time from a time measuring device (not shown) included in the abnormality diagnosis device 2 ( In step S201, the set time stored in advance in the storage unit 20 is read and compared with the acquired current time (step S202). If the current time has passed the set time (step S202 “Yes”), the diagnosis request generation unit 211 generates a diagnosis request signal and activates the processing of the driving state determination unit 212 (step S203). ) If the current time has not passed the set time (step S203 “No”), the diagnosis request generation process is terminated.

  FIG. 3 is a flowchart showing a processing procedure of the operation state determination unit 212 in the abnormality diagnosis device 2. The function of the operation state determination unit 212 acquires the operation state information and cage weight information of the cage from the elevator controller 11, and collects environmental sounds after monitoring that the cage is stopped and no operation noise is generated. Then, it is determined whether or not the elevator 1 is in a state where an abnormality can be diagnosed.

  The processing of the driving state determination unit 212 is activated by a diagnosis request signal generated by the diagnosis request generation unit 211, and first acquires the driving state information and cage weight of the elevator 1 from the elevator controller 11, as shown in FIG. (Step S301). Subsequently, the operation state determination unit 212 determines whether or not the elevator 1 is stopped from the operation state information of the elevator 1 acquired from the elevator control device 11 (step S302).

  As a result of the determination in step S302, if the elevator 1 is in the lifting operation (step S302 “No”), the operating state determination unit 212 waits for a certain time (step S305), and then executes the process of step S301 again. The operation state information is acquired from the elevator control device 11. In addition, when the elevator 1 is stopped (step S302 “Yes”), the operating state determination unit 212 further detects the weight of the cage from the elevator controller 11 in order to detect that no person is on the cage. Is determined to be within a predetermined reference value (step S303). Here, the reference value of the cage weight is a cage weight set when a person is not riding in the cage with a certain width, and is set in a predetermined area of the storage unit 20 in advance.

  Next, when it is determined that the cage weight is out of the reference value because a person is on the elevator 1 (step S303 “No”), the driving state determination unit 212 executes the process of step S301 again. Then, the operation state information is acquired from the elevator control device 11. On the other hand, when it is determined that no person is in the cage, that is, it is determined that the cage weight is within the reference value (step S303 “Yes”), the driving state determination unit 212 performs processing of the measurement environment difference calculation unit 213. Is activated (step S304).

  FIG. 4 is a flowchart showing a processing procedure of the measurement environment difference calculation unit 213 in the abnormality diagnosis apparatus 2. The function of the measurement environment difference calculation unit 213 is to collect environmental sounds generated in the surroundings, and compare these with the reference state to determine whether or not the environment is diagnosable. The process of the measurement environment difference calculation unit 213 is started by the above-described operation state determination unit 212.

  As shown in FIG. 4, the measurement environment difference calculation unit 213 collects an acoustic signal (hereinafter referred to as “real recording environment sound”) via the sensor 12 (step S <b> 401), and refers to a predetermined area of the storage unit 20. Then, an acoustic signal in the ideal environment (hereinafter referred to as “ideal environmental sound”), which is reference data for executing the abnormality diagnosis process, is acquired (step S402). Next, the measurement environment difference calculation unit 213 calculates a measurement environment difference that is the difference between the acquired actual environmental sound and the ideal environmental sound (step S403), and then activates the process of the diagnosis availability determination unit 214. (Step S404).

  FIG. 5 is a block chart showing a flow of signal processing for measurement environment difference calculation by the measurement environment difference calculation unit 213 in the abnormality diagnosis apparatus 2. Hereinafter, a method of calculating the measurement environment difference will be described with reference to FIG.

  As shown in FIG. 5, the measurement environment difference calculation unit 213 performs a Fourier transform 51 using the ideal environment sound as input data, and further performs a cluster analysis 52 of the frequency spectrum obtained as a result. Similarly, the Fourier transform 51a and the cluster analysis 52a are performed for the real environment sound. Then, the cluster identification process 53 calculates the measurement environment difference based on the data after each cluster analysis 52, 52a.

  That is, the measurement environment difference calculation unit 213 converts an acoustic signal that changes over time into a spectrum of a frequency component by Fourier transforms 51 and 51a. At this time, the frequency spectrum at each time can be calculated by shifting the window function in the time direction at every Fourier transform.

  Next, the measurement environment difference calculation unit 213 plots the size of the frequency spectrum at each time on the coordinate space. Here, the coordinate axis is each frequency after Fourier transform.

FIG. 6 is an explanatory diagram for explaining an overview of the cluster analysis 52, 52a by the measurement environment difference calculation unit 213. First, the method of plotting on the coordinate space will be described with reference to FIG. In FIG. 6A, for simplification of explanation, the spectral frequencies after Fourier transform are only f ₁ and f ₂ and the coordinate space is two-dimensional, but in general, it is n-dimensional.

The measurement environment difference calculation unit 213 converts the acoustic signal at time t ₁ into a frequency spectrum by Fourier transforms 51 and 51a. As a result, the spectrum size of the frequency f ₁ is x ₁ and the spectrum size of the frequency f ₂ is for y _1, plotting the frequency spectrum into coordinates on the coordinate space (x _1, y _1). Plot all those Fourier transform Similarly for the time t ₂ after the acoustic signal.

  The measurement environment difference calculation unit 213 groups the data plotted in the coordinate space. For the grouping, for example, a well-known Ward's method is used, and grouping, that is, clustering, is performed until the number of clusters reaches a certain value. In addition, as a clustering method, the shortest / longest distance method (Nearest / Furthest Neighbor Method), the group average method, etc. which are other well-known techniques may be used instead of the Ward method.

  Next, the measurement environment difference calculation unit 213 sets a minimum sphere (generally, an n-dimensional sphere or a circle in the case of 2D) that includes all plots in each cluster, and the center coordinates and radius thereof. Is calculated. Here, with reference to FIG. 6B, a method of calculating the center coordinates and radius of the sphere will be described.

FIG. 6B shows a case where the plot is grouped into _three clusters (C ₁ , C ₂ , C ₃ ). In this case, the center coordinates and the radius of the sphere to be set, respectively, the center coordinates of the cluster C ₁ is (C _x1, C _y1), the radius r _1, the center coordinates of the cluster C ₂ is (C _x2, C _y2), The radius is r ₂ , the center coordinates of the cluster C ₃ are (C _x3 , C _y3 ), and the radius is r ₃ . Then, the measurement environment difference calculation unit 213 sets the center coordinates and the radius calculated in this way for each cluster number in the cluster information table 201 having a data structure as shown in FIG. The cluster information table 201 is assigned to a predetermined area of the storage unit 20.

  In the cluster identification process 53, the measurement environment difference calculation unit 213 compares the cluster information table 201 that has processed the ideal environmental sound with the cluster information table 201 that has processed the actual recording environmental sound, thereby determining the identification of the cluster of each environmental sound. Do. The identification determination method will be described below with reference to the flowchart shown in FIG.

  FIG. 8 is a flowchart showing the cluster identification processing procedure of the measurement environment difference calculation unit 213 in the abnormality diagnosis apparatus 2. As shown in FIG. 8, the measurement environment difference calculation unit 213 first refers to the data of the cluster information table 201 of the ideal environmental sound and performs a loop process for each cluster number (steps S101 to S109). In this loop processing, first, data relating to the center coordinates and radius of each cluster is acquired (step S102). Next, the cluster information table 201 of the real environment sound is referred to (step S103), and loop processing is performed for each cluster number (steps S103 to S108). That is, the measurement environment difference calculation unit 213 evaluates the positional relationship by collating the center coordinates of the cluster of the ideal environment sound with the center coordinates of the cluster of the actually recorded environment sound (step S104).

  Here, the measurement environment difference calculation unit 213 calculates the following equation (1) in order to collate the center coordinates. Then, the measurement environment difference calculation unit 213 temporarily stores in the storage unit 20 whether or not Expression (1) is satisfied.

ここで、nはフーリエ変換後の周波数成分の数、fsは実録環境音データのs番目のクラスタの中心座標の成分、fiは理想環境音データのi番目のクラスタの中心座標の成分、riは理想環境音データのi番目のクラスタの半径である。

Here, n is the number of frequency components after Fourier transform, fs is the component of the center coordinate of the s-th cluster of the recorded environmental sound data, fi is the component of the center coordinate of the i-th cluster of the ideal environmental sound data, and ri is This is the radius of the i-th cluster of ideal environmental sound data.

  Next, the measurement environment difference calculation unit 213 calculates a radius ratio (step S105). The radius ratio is calculated by executing the following formula (2), and the calculation result here is temporarily stored in a predetermined area of the storage unit 20.

ここで、ratioは半径比率、rsは実録環境音データのs番目のクラスタの半径、riは理想環境音データのi番目のクラスタの半径である。

Here, ratio is the radius ratio, rs is the radius of the s-th cluster of the recorded environmental sound data, and ri is the radius of the i-th cluster of the ideal environmental sound data.

Subsequently, the measurement environment difference calculation unit 213 determines whether or not the i-th cluster of the ideal environmental sound and the s-th cluster of the recorded environmental sound match based on the calculation results of the expressions (1) and (2). Is determined (step S106). Specifically, measurement environment difference calculating unit 213 satisfies the formula (1), and, ratio constant value determined by the formula (2) (e.g., 1.5) or less is if, those the ideal environment It is determined that the cluster of the sound and the cluster of the recording environment sound are the same (step S106 “Yes”), and the check table 202 shown in FIG. 9 is updated (step S107). The check table 202 is assigned to a predetermined area of the storage unit 20.

  FIG. 9 is a diagram illustrating an example of the data structure of the check table 202. As shown in FIG. 11, the cluster number of the actual environmental sound is recorded in the cluster number column of the check table 202, and “1” is set in advance in all the determination columns. The measurement environment difference calculation unit 213 rewrites the value in the determination column from “1” to “0” for the cluster number of the cluster of the recorded environment sound that is determined to be the same as the cluster of the ideal environment sound. Execute. As a result, the value in the determination column is “1” only for the cluster numbers that are not determined to be the same as the cluster of the ideal environmental sound.

  The measurement environment difference calculation process (FIG. 4, step S403) is completed by the above process, and the measurement environment difference calculation unit 213 next activates the process of the diagnosis availability determination unit 214 (step S404).

  FIG. 10 is a flowchart illustrating a processing procedure of the diagnosis availability determination unit 214 in the abnormality diagnosis device 2 according to the first embodiment of the present invention. The function of the diagnosis availability determination unit 214 is to receive the result of the environmental difference evaluated by the measurement environment difference calculation unit 213 and determine whether or not the abnormality diagnosis can be performed.

  As shown in FIG. 10, the measurement environment difference calculation unit 213 first refers to the check table 202 (see FIG. 9) generated by the measurement environment difference calculation unit 213 and stored in the storage unit 20 (step S501). Then, it is determined whether all the determination columns of the check table 202 are “0” (step S502).

  When there is “1” in any of the determination columns (step S502 “No”), the diagnosis availability determination unit 214 is in an environment where the actual recording environmental sound is different from the ideal environmental sound and abnormality diagnosis is impossible. Is determined. Here, when it is determined that the environment cannot be diagnosed, the diagnosis availability determination unit 214 activates the processing of the operation state determination unit 212 (step S504), and the measurement environment difference calculation unit 213 calculates the measurement environment difference. Find out and evaluate if you are in a diagnostic environment again. When evaluating whether or not the environment can be diagnosed again, the cage stop position of the elevator 1 can be moved to evaluate whether or not the environment can be diagnosed on another floor. Good.

  On the other hand, when all the determination columns are “0” (step S502 “Yes”), the diagnosis availability determination unit 214 determines that the actually recorded environmental sound is similar to the ideal environmental sound and is an environment in which an abnormality can be diagnosed. The processing of the processing unit 215 is activated (step S503).

  FIG. 11 is a flowchart showing a processing procedure of the diagnosis processing unit 215 in the abnormality diagnosis device 2 according to the first embodiment of the present invention. The function of the diagnosis processing unit 215 is to perform abnormality diagnosis by converting acoustic signals collected from the sensor 12 included in the elevator 1 into frequency components and comparing the magnitude of each frequency component with a reference value.

  As illustrated in FIG. 11, the diagnosis processing unit 215 first operates the elevator 1 to be diagnosed, and collects acoustic signals that are operation sounds of the elevator 1 from the sensor 12 (step S601). Subsequently, the diagnosis processing unit 215 converts the collected acoustic signal into a frequency component by Fourier transform. Next, the diagnosis processing unit 215 reads a preset abnormality determination threshold value from the storage unit 20 (step S602), compares the magnitude of each frequency component subjected to Fourier transform and the abnormality determination threshold value, and is larger than the abnormality determination threshold value. If there is a frequency component, an abnormality determination process for determining that there is an abnormality is executed (step S603).

(Effects of the first embodiment)
According to the above-described mobile body abnormality diagnosis apparatus 2 according to the first embodiment of the present invention, when an abnormality diagnosis is performed using an acoustic signal emitted from a mobile body such as the elevator 1 to be diagnosed, After confirming that the elevator 1 is in a stopped state and no operation noise is generated, the ambient environmental sound is collected, and the ambient environmental sound can be diagnosed by arithmetic processing using Fourier transform and cluster analysis. In the case where it is determined that there is, the elevator 1 is operated to collect the operation sound and perform the abnormality diagnosis process.

  Therefore, in the present embodiment, the operation sound of the moving body is collected in the state where there is no environmental sound causing misdiagnosis, that is, when the surrounding acoustic state becomes a state where an abnormality diagnosis is possible, Since abnormality diagnosis processing is performed, the reliability of abnormality diagnosis can be improved.

  When it is determined that the environmental sound cannot be diagnosed, after waiting for a predetermined time or by moving the elevator 1 by a predetermined distance, that is, moving to another floor, the diagnosis is possible. At that time, the elevator 1 is operated to collect operation sounds, and abnormality diagnosis processing is performed. As a result, the reliability can be further improved.

<Second Embodiment>
Subsequently, an abnormality diagnosis apparatus for a moving body according to a second embodiment of the present invention will be described. The block-level configuration of the moving body abnormality diagnosis apparatus according to the second embodiment of the present invention is the same as the block configuration (see FIG. 1) of the abnormality diagnosis apparatus shown in the first embodiment. Therefore, the block configuration of the abnormality diagnosis apparatus shown in FIG. 1 is also used here. The difference from the first embodiment lies in the processing contents of the measurement environment difference calculation unit 213 and the diagnosis availability determination unit 214. Hereinafter, in order to avoid duplication of description, the description will be made focusing on differences from the first embodiment.

(Operation of Second Embodiment)
FIG. 12 is a flowchart showing a processing procedure of the measurement environment difference calculation unit 213 in the abnormality diagnosis apparatus 2 according to the second embodiment of the present invention. As shown in FIG. 12, the measurement environment difference calculation unit 213 first collects actual recording environment sounds from the sensor 12 (step S121). Next, the measurement environment difference calculation unit 213 reads reference data in which a value is set for each frequency as the frequency characteristic of the ideal environment sound from the storage unit 20 (step S122), and is a difference between the actually recorded environment sound and the reference data. A measurement environment difference is calculated (step S123). Next, this calculation method will be described in detail with reference to FIG. Will be described with reference to a block chart showing a flow of signal processing for the measurement environment difference by the measurement environment difference calculation unit 213 shown in FIG.

  FIG. 13 is a block chart showing a flow of signal processing for measurement environment difference calculation by the measurement environment difference calculation unit 213 in the abnormality diagnosis apparatus 2 according to the second embodiment of the present invention. As shown in FIG. 13, when calculating the measurement environment difference, the measurement environment difference calculation unit 213 performs Fourier transform 1301 on the ideal environment sound and converts the ideal environment sound into components for each frequency. Then, the difference calculation 1302 calculates a measurement environment difference for each frequency and outputs the difference to the diagnosis availability determination unit 214. The calculation formula of the measurement environment difference is expressed by the following formula (3).

ここで、nは、フーリエ変換後の周波数の順番、Ad_ｎは、測定環境差(周波数別)、As_nは、実録環境音をフーリエ変換した後の値（周波数別）、Ai_nは、基準データの値（周波数別）である。

Here, n is the order of the frequency of the Fourier transform, Ad _n is measured environmental difference (frequency-), As _n, the value after the Fourier transform of the Annals environmental sound (frequency-), Ai _n is the reference Data values (by frequency).

The measurement environment difference Ad _n calculated using Expression (3) is stored in the measurement environment difference table 203 shown in FIG. Here, FIG. 14 is a diagram showing an example of the data structure of the measurement environment difference table in the abnormality diagnosis apparatus according to the second embodiment of the present invention. The contents of the measurement environment difference table 203 are temporarily stored in a predetermined area of the storage unit 20.

  The description returns to FIG. 12 again. As described above, the measurement environment difference calculation unit 213 starts the diagnosis availability determination unit 214 after calculating the measurement environment difference (step S124). The function of the diagnosis availability determination unit 214 is to evaluate the difference evaluated by the measurement environment difference calculation unit 213 and determine whether or not the abnormality diagnosis of the elevator 1 can be performed.

FIG. 15 is a flowchart illustrating a processing procedure of the diagnosis availability determination unit in the abnormality diagnosis apparatus according to the second embodiment of the present invention. As shown in FIG. 15, the diagnosis availability determination unit 214 first refers to the measurement environment difference table 203 temporarily stored in the storage unit 20 (step S151), reads all the measurement environment differences Ad _n , and all of them. The measurement environment difference Ad _n is compared with a threshold value preset and held in a predetermined area of the storage unit 20 (step S152).

Here, when there is a measurement environment difference Ad _n having a value larger than the threshold value (step S152 “No”), the diagnosis availability determination unit 214 has a large divergence between the actual recording environment sound and the ideal environment sound, and the abnormality diagnosis is impossible. As in the case of the first embodiment, the operating state determination unit 212 is activated (step S154), the measurement environment difference calculation unit 213 obtains the measurement environment difference, and the environment is diagnosable again. Evaluate whether or not. When evaluating whether or not the environment can be diagnosed again, as in the case of the first embodiment, the environment where the abnormality can be diagnosed on another floor by moving the stop position of the cage of the elevator 1. It will be evaluated whether or not.

On the other hand, when there is no measurement environment difference Ad _n having a value larger than the threshold value (step S152 “Yes”), the diagnosis availability determination unit 214 determines that the actual recording environment sound is similar to the ideal environment sound and allows an abnormality diagnosis. It is determined that there is, and the diagnosis processing unit 215 is activated (step S153), and the diagnosis process is executed. Subsequent processing is the same as that in the first embodiment, and a description thereof will be omitted.

(Effect of 2nd Embodiment)
According to the above-described mobile body abnormality diagnosis apparatus according to the second embodiment of the present invention, when an abnormality diagnosis is performed using a sound signal generated by the mobile body, for example, ambient sound is first collected. Compare this with the reference state to determine whether the environment is diagnosable, and if it is determined that the environment sound is low and the environment is diagnosable, operate the mobile object and collect the operation sound to detect abnormalities. Execute diagnostic processing. Therefore, since the operation sound of the moving body can be collected in the state where there is no environmental sound causing misdiagnosis, it is possible to prevent erroneous diagnosis due to the environmental sound.

<Other embodiments>
As described above, in the first and second embodiments, only the case where an acoustic signal is used as a physical signal to be diagnosed has been described. However, not only the acoustic signal but also vibration data (acceleration signal) and moving speed data are used. In addition, the abnormality diagnosis apparatus 2 can be configured by the same processing. Moreover, in 1st and 2nd embodiment, although the elevator 1 was illustrated as a moving body to which the abnormality diagnosis apparatus 2 is applied, it is also possible to apply not only to the elevator 1 but also to a moving body such as a vehicle. . Furthermore, the present invention can be applied to fixed equipment such as a turbine and a boiler.

  In the first and second embodiments, the functions of the arithmetic processing unit 21 shown in FIG. 1 are realized by program processing, that is, software. However, all or some of the functions are implemented by hardware. It may be realized by hardware (such as a dedicated electronic circuit).

  For example, when there is a diagnosis request, the arithmetic processing unit 21 acquires a physical signal emitted from the periphery of the moving body in a state where the moving body stops driving, and stores the acquired physical signal and the storage unit 20 in the acquired physical signal. It is determined whether or not the moving body is in an diagnosable environment in comparison with the physical signal generated, and if it is determined that the moving body is in an diagnosable environment, the physics emitted according to the operating state of the moving body Data processing that acquires signals and performs analysis based on predetermined computations to diagnose abnormalities of moving objects may be realized on a computer by one or more programs, and at least a part thereof is realized by hardware May be.

  As described above, the present invention identifies time zones when there are no people, such as elevators installed in facilities such as department stores and railway stations, as well as elevators installed in facilities such as apartment buildings and residential buildings. Is suitable for use in difficult cases. Further, the present invention can be applied not only to moving elevators and vehicles but also to fixed devices such as turbines.

DESCRIPTION OF SYMBOLS 1 Elevator 2 Abnormality diagnosis apparatus 11 Elevator control apparatus 12 Sensor 20 Storage part 21 Arithmetic processing part 201 Cluster information table 202 Check table 203 Measurement environment difference table 211 Diagnosis request generation part 212 Operation state determination part 213 Measurement environment difference calculation part 214 Diagnosability Determination unit 215 Diagnosis processing unit

Claims (8)

An abnormality diagnosing device that detects a physical signal generated according to the operating state of a moving body, analyzes the detected physical signal by a predetermined calculation, and diagnoses it,
A storage unit storing physical signals in a reference diagnostic environment;
Obtaining a physical signal emitted from the periphery of the moving body in a state where the moving body has stopped driving when receiving a diagnosis request;
The acquired physical signal is converted into a frequency component to perform cluster analysis, and each radius and center coordinates of one or more clusters belonging to the first group obtained by the cluster analysis are calculated and stored in the storage unit. The obtained physical signal is converted into frequency components to perform cluster analysis, and the radius and center coordinates of each of one or more clusters belonging to the second group obtained by the cluster analysis are calculated,
By comparing the respective radii and center coordinates of the clusters belonging to the first group with the respective radii and center coordinates of the clusters belonging to the second group, the clusters belonging to the first group are compared with the first group. Identify whether it is the same as the cluster belonging to group 2,
As a result of the identification, if the cluster belonging to the first group is identified as the same as the cluster belonging to the second group, it is determined that the moving body is in an diagnosable environment, and the moving body is is the operating state, an arithmetic processing unit which acquires the physical signal generated in accordance with the operating condition of its analyzes based on the predetermined operation, the abnormality diagnosis of the movable body,
An abnormality diagnosis device comprising: The arithmetic processing unit includes:
As a result of the identification, if the cluster belonging to the first group is not identified as the same as the cluster belonging to the second group, it is determined that the mobile body is not in a diagnosable environment, and a predetermined time has elapsed. The abnormality diagnosis device according to claim 1 , wherein a physical signal emitted from the periphery of the moving body is acquired later and it is determined again whether or not the moving body is in a diagnosable state. The arithmetic processing unit includes:
As a result of the identification, if the cluster belonging to the first group is not identified as the same as the cluster belonging to the second group, it is determined that the mobile body is not in a diagnosable environment, and the mobile body The physical signal emitted from the periphery of the moving body is acquired after moving the vehicle by a predetermined distance, and it is determined again whether the moving body is in a state where it can be diagnosed. Abnormality diagnosis device. The abnormality diagnosis device according to claim 1, wherein the physical signal acquired by the arithmetic processing unit is an acoustic signal or an acceleration signal. At least a storage unit and an arithmetic processing unit are provided, and a physical signal generated according to a driving state of the moving body is detected by a sensor, and the detected physical signal is analyzed by the arithmetic processing unit executing a predetermined calculation. An abnormality diagnosis method used in an abnormality diagnosis apparatus that performs diagnosis,
The arithmetic processing unit includes:
Acquiring a physical signal in a reference diagnostic environment via the sensor and storing the physical signal in the storage unit;
And Luz step to obtain the physical signals emanating from near the movable body in a state in which the movable body at the time of diagnosis request acceptance has stopped operating,
The acquired physical signal is converted into a frequency component to perform cluster analysis, and each radius and center coordinates of one or more clusters belonging to the first group obtained by the cluster analysis are calculated and stored in the storage unit. Performing a cluster analysis by converting the physical signal thus obtained into a frequency component, and calculating respective radii and center coordinates of one or more clusters belonging to the second group obtained by the cluster analysis;
By comparing the respective radii and center coordinates of the clusters belonging to the first group with the respective radii and center coordinates of the clusters belonging to the second group, the clusters belonging to the first group are compared with the first group. Identifying whether it is the same as a cluster belonging to group 2;
As a result of the identification, if the cluster belonging to the first group is identified as the same as the cluster belonging to the second group, it is determined that the moving body is in an diagnosable environment, and the moving body is is the operating state, to obtain a physical signal generated in accordance with the operating condition of its analyzes based on the predetermined operation, the row mortar steps abnormality diagnosis of the movable body,
An abnormality diagnosis method characterized in that The arithmetic processing unit includes:
As a result of the identification, if the cluster belonging to the first group is not identified as the same as the cluster belonging to the second group, it is determined that the mobile body is not in a diagnosable environment, and a predetermined time has elapsed. 6. The abnormality diagnosis method according to claim 5 , wherein a physical signal emitted from the periphery of the moving body is acquired and it is determined again whether or not the moving body is in a diagnosable state. The arithmetic processing unit includes:
As a result of the identification, if the cluster belonging to the first group is not identified as the same as the cluster belonging to the second group, it is determined that the mobile body is not in a diagnosable environment, and the mobile body 6. The physical signal emitted from the periphery of the moving body is acquired after moving a predetermined distance, and it is determined again whether or not the moving body is in a diagnosable state. Abnormal diagnosis method. The abnormality diagnosis method according to any one of claims 5 to 7 , wherein the physical signal acquired by the arithmetic processing unit is an acoustic signal or an acceleration signal.

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