KR101962558B1 - Abnormal sound diagnostic apparatus, abnormal sound diagnostic system, abnormal sound diagnostic method, and abnormal sound diagnostic program - Google Patents
Abnormal sound diagnostic apparatus, abnormal sound diagnostic system, abnormal sound diagnostic method, and abnormal sound diagnostic program Download PDFInfo
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Abstract
A trajectory feature extraction unit 5 for extracting a trajectory vector 15 by converting a trajectory showing the intensity characteristics in the entire time direction of the strength time series 14 acquired by the strength time series acquisition unit 4 into a vector, And an identification parameter storage section for storing the learned identification parameter (16) by outputting information indicative of a state type of the diagnosis object device, An identification unit 7 for obtaining a K-dimensional score vector 17 for each state type of the diagnostic target apparatus from the locus vector 15 and the identification parameter 16, a K-dimensional score vector 17 And a judging section 8 for judging whether or not the sound generated in the diagnosis target device is normal or abnormal.
Description
The present invention is an abnormality diagnosis apparatus for analyzing sounds generated from a device to be diagnosed and diagnosing the types of abnormal noises and abnormal sounds generated by the device, An abnormal sound diagnostic system, an abnormal sound diagnostic method, and an abnormal sound diagnostic program.
Conventionally, as an abnormal sound diagnostic apparatus, an analysis result of sound data collected in a state in which a device to be diagnosed normally operates is stored as a reference value, and the analysis result of the sound data collected at the time of diagnosis deviates from the stored reference value It is known to diagnose that an abnormality has occurred in the device.
For example, the abnormal sound detection device disclosed in
The abnormality diagnosis apparatus disclosed in
However, in the above-described technologies of
For this reason, in the case where it is not possible to pick up the sound during normal operation before the diagnosis of the apparatus, for example, in the case of an elevator installed at the time of contract, the criteria for diagnosis can not be prepared, There is a problem that it can not be done.
In addition, as described above, when it is not possible to collect a sound during normal operation and a reference for diagnosis can not be prepared, a sound for normal operation is collected by using another apparatus having the same specification, and a criterion for diagnosis is prepared You can also think of how to do it. However, in the case of a complicated device composed of a large number of parts, the specifications such as the installation position of the sound collector, the size of the components constituting the device, and the arrangement conditions of the device, such as the height of the building, It is not practical to prepare a device that is set to have the same specifications such as size, material of the hoistway, cargo load, speed of operation, etc., and it is difficult to prepare appropriate standards using other devices There was a challenge.
An object of the present invention is to diagnose the operation state of the device without requiring the sound collection during the normal operation in advance for the device to be diagnosed.
The abnormal sound diagnostic apparatus according to the present invention includes a sound collecting section for collecting sounds generated in a diagnosis target device and acquiring sound data, a strength time series acquiring section for acquiring an intensity time series from a time frequency distribution obtained by analyzing waveform data of sound data, A trajectory feature extracting unit for extracting a trajectory vector by converting a trajectory showing the intensity characteristics in the entire time direction of the strength time series into a vector and extracting a trajectory vector from the time frequency distribution obtained by analyzing the waveform data of the sound data generated from the reference device An identification parameter storage section for storing a learned identification parameter as an input of a vector which is a locus indicating a strength characteristic in the entire time direction of the strength time series and outputting information indicating a state type of the diagnosis target device; A score for each state type of the diagnostic target device And a judging section for judging whether or not the sound generated in the diagnostic target apparatus is normal or abnormal with reference to the score.
According to the present invention, it is possible to diagnose the presence or absence of an abnormal sound even for a device which can not collect a sound during normal operation and can not create a criterion for diagnosis.
BRIEF DESCRIPTION OF DRAWINGS FIG. 1 is a block diagram showing the configuration of an abnormal sound diagnostic apparatus according to a first embodiment. FIG.
2 is a diagram showing a configuration of an identification unit of the abnormal sound diagnostic apparatus according to the first embodiment.
3 is a block diagram showing a configuration of an identification parameter learning apparatus according to the first embodiment.
4 is a diagram showing an example of accumulation of a database of an identification parameter learning apparatus according to the first embodiment.
5 is a flowchart showing the operation of the abnormal sound diagnostic apparatus according to the first embodiment.
6 is a flowchart showing the operation of the abnormal sound diagnosis apparatus according to the first embodiment.
7 is a diagram showing an example of an anomaly type and a K-dimension score vector referenced by the determination section of the abnormal-sound diagnostic apparatus according to the first embodiment.
8 is an explanatory diagram showing the effect of the abnormality diagnosis by the abnormality diagnosis apparatus of the first embodiment;
Fig. 9 is an explanatory view showing the result of abnormal sound diagnosis by a conventional abnormal sound diagnosis apparatus; Fig.
10 is an explanatory diagram showing the connection of a plurality of intensity vectors by the trajectory feature extraction unit of the abnormality diagnosis apparatus according to the first embodiment;
11 is an explanatory diagram showing the effect of the abnormality sound diagnosis according to the other constitution of the abnormality diagnosis apparatus of the first embodiment;
12 is a diagram showing a configuration of an identification unit of the abnormal sound diagnosis apparatus according to the second embodiment.
13 is a flowchart showing the operation of the abnormality diagnosis apparatus according to the second embodiment.
BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, embodiments for carrying out the present invention will be described with reference to the accompanying drawings in order to explain the present invention in more detail.
The abnormality diagnosis apparatus according to the first embodiment diagnoses a sound generated from a device (for example, an elevator or the like) to be diagnosed and determines whether or not the generated sound is a normal sound or more, . The device to be diagnosed is a device composed of a plurality of movable parts such as an elevator, for example. The sound collecting means for collecting the generated sound is attached to the inside or outside of the car of the elevator so that the sound generated when the car reciprocates And determines whether the sound collected and collected is normal or abnormal, thereby diagnosing the moving sound of the movable part. Further, the abnormality diagnosis apparatus of the present invention can be applied to other than the elevator.
In the following, a case where the abnormal sound diagnostic apparatus is implemented as software on a personal computer (hereinafter referred to as PC) will be described as an example. The PC has a USB terminal and a LAN terminal, a microphone is connected to the USB terminal through an audio interface circuit, and a device to be diagnosed is connected to the LAN terminal through a LAN cable. The device to be diagnosed is configured to perform a predetermined driving operation by a control instruction output from the PC. Further, the abnormal sound diagnostic apparatus 100 is not limited to the case where it is implemented as software, and can be appropriately changed.
1 is a block diagram showing a configuration of an abnormal-sound diagnostic apparatus 100 according to the first embodiment.
Fig. 1 (a) is a functional block diagram of the abnormal sound diagnosis apparatus 100 according to the first embodiment, which includes a
The
The time
The
Fig. 1B is a block diagram showing a hardware configuration of the abnormal-sound diagnostic apparatus 100 according to the first embodiment, which is composed of a
Next, the detailed configuration of the
2 is an explanatory view showing the configuration of the
The neural network shown in the example of Fig. 2 is configured in a hierarchical structure, and is composed of one
The last hidden layer also serves as an output layer. In the example of Fig. 2, the second hidden
The
In Equation (1), U (m) represents the number of units of the m-th layer.
The load and bias necessary for calculating the response of the hidden layer are supplied from the
Next, learning of the
3 (a) is a functional block diagram of the identification
The sound
An example of the
4, the
As an example of the abnormal class C (v), "normal", "abnormal", "abnormal", and "abnormal middle layer" are associated with each other. The above types are set.
The waveform acquisition unit 23 samples the waveform of the
The identification learning unit 28 creates learning data for learning a neural network. Learning data of a neural network generally consists of input data and output data expected to be output by the neural network when the input data is given. In the example of the block diagram shown in Fig. 3, the input data is the trajectory vector 34 input from the trajectory
Assuming that the total number of sound data used for learning of the neural network is V, the input data is V trajectory vectors 34 and the output data is V teacher vectors 35. [
The input data x (k, v) in the discrimination learning section 28 is expressed by the following equation (7), where ρ (k, v) is the locus vector 34 extracted from the v- (2).
That is, the input data x (k, v) is the same as the trajectory vector 34.
The V teacher vectors 35 generated by the
The identification learning unit 28 performs learning of the neural network by using the trajectory vector 34 which is the input data obtained as described above and the teacher data vector 35 as the output data, And stores the obtained parameter in the identification
3 (b) is a block diagram showing the hardware configuration of the identification
Next, the operation of the abnormal sound diagnostic apparatus 100 will be described with reference to Figs. 5 and 6. Fig.
5 and 6 are flowcharts showing the operation of the abnormal sound diagnosis apparatus 100 according to the first embodiment. Fig. 5 shows the operation of the
When the abnormal sound diagnostic apparatus 100 detects the start of operation of the apparatus (step ST1), the
Next, the abnormal-sound diagnostic apparatus 100 determines whether or not the operation of the apparatus has been terminated (step ST5). If the operation of the device has not been terminated (step ST5; NO), the process returns to step ST2 and the above-described process is repeated. On the other hand, when the operation of the apparatus is ended (step ST5; YES), the
The time
Here, t is the time index corresponding to the shift interval for shifting the time window, and f is an index indicating the frequency of the result of the FFT operation. The time t and the frequency f are integers satisfying 0? T? T and 0? F? F, respectively. T is the number of frames in the time direction of the
Next, the intensity time
In Expression (4), b is an index of a band, and is an integer satisfying 0? B <B (where B is the number of bands, B = 5 in this example). Further, Ω (b) represents a set of frequencies f to be searched for the band b in the time-frequency distribution g (t, f).
The trajectory
The smoothed time series G ~ (t, b) (t = 0, 1, ..., T-1, b = 0, 1, ..., B- .
In equation (5), smooth_t (x (t)) is a function that outputs a new time series obtained by smoothing series x (t) related to t in the subscript t direction.
The smoothing intensity H (l, b) (l = 0, 1, ..., L-1, b = 0, 1, ..., B-1) of the L- Is calculated based on equation (6).
In Equation (6), τ (l) is a real number function indicating an interpolation position related to the subscript t in G ~ (t, b), and w (l) is a function for assigning a weighting coefficient in interpolation. (7) and (8).
Int (x) in the expression (8) is a function for obtaining the integer part of the argument x.
In generating the
Next, the
The processing in step ST17 will be described with reference to a specific configuration example of the
In Expression (10), p (i) represents the value of the i-th element of the
Next, the outputs of the respective units are sequentially calculated from the
In Equation (11),? (X) is a sigmoid function having a nonlinear input / output characteristic representing the threshold characteristic of soft, and is given by the following Expression (12).
In the above-described equation (11), when m = 1, x (i, 0) is required. This is because the i-th element ρ i.
By performing the calculation based on the equation (11) up to m = 1, ..., M, the output x (k, M) of the last hidden layer is obtained. In the example of FIG. 2, the output x (k, 2) of the second hidden
Finally, the K outputs of the output layer are normalized. By normalization, the total sum of the K outputs becomes 1. Assuming that the result of the normalization is the value s (k) of the score vector, the value s (k) of the score vector is given by the following equation (14) known as the softmax operation.
The K-
Returning to the flowchart of Fig. 6, description will be continued.
The determining
In addition, although a configuration is shown in which the score of the K-
7 is a diagram showing an example of an abnormal type and a K-dimensional score vector referenced by the
As shown in Fig. 7, K " abnormal types " are associated with " K dimension score vectors ", respectively. The K-dimensional score vector becomes " 1 " by adding all the values of the K score vectors constituting the K-dimensional score vector. In the example of Fig. 7, since the score vector of the abnormal type "abnormal part" takes a maximum value of "0.64", the judging
Next, the effect when the abnormality diagnosis apparatus 100 configured as described above is applied to an elevator will be described with reference to Figs. 8 and 9. Fig.
8 is an explanatory diagram showing the effect of the abnormal sound diagnosis by the abnormal sound diagnosis apparatus 100 according to the first embodiment. In addition, as a comparison, FIG. 9 shows the result of the abnormal sound diagnosis by the conventional abnormal sound diagnosis apparatus.
First, the method of the abnormal sound diagnosis by the conventional abnormal sound diagnostic apparatus and the obtained result will be described with reference to Fig. In the conventional abnormality diagnosis apparatus, the running
By comparing the stored reference value with the intensity time series of the sound data obtained at the time of diagnosis, the above detection was performed in each section. Since the negative signal strength at the normal time in each section differs depending on the use or the operating environment of each elevator, the reference value obtained for an elevator can not be applied to the diagnosis of an abnormal sound of an elevator, There is a problem that the accuracy of the abnormality diagnosis is lowered. Therefore, in the conventional abnormality diagnosis apparatus, it is necessary to perform the learning operation in advance for each elevator, and to store the reference value.
Fig. 9 (b) is a diagram prepared based on the negative signal strength at the time of diagnosis when the reference value created in different elevators is applied to another elevator and the result of the comparison is compared with the negative signal strength at the time of diagnosis. There is an
Next, the effect of the abnormality sound diagnosis by the abnormality sound diagnosis apparatus 100 according to the first embodiment will be described with reference to FIG.
In the abnormal sound diagnosis apparatus 100 according to the first embodiment, as shown in Fig. 8 (a), the sound generated while the
Fig. 8 (b) is a graph showing the relationship between the first characteristic axis (main axis) and the second characteristic axis (axis perpendicular to the main axis) And the arrangement of each vector is shown on an L x 1 -dimensional space formed by the characteristic axes thereof.
Principal component analysis is a process for displaying the positional relationship of mutualities in a multidimensional space of a vector, and is not a process constituting the present invention. The first characteristic axis and the second characteristic axis are not calculated by the configuration of the present invention, but are described to indicate that the locus vector is classified in space.
In the case where the
8 (b) shows an example in which a straight line is obtained as the
As described above, it is possible to grasp the general characteristics represented by the strength time series, which do not depend on the elevator specifications and the operating environment, so that it is not necessary to learn the reference values for each individual in advance and the robustness of the elevator specifications and operating environment ) Diagnosis can be performed.
As described above, according to the first embodiment, the
In the above description of the first embodiment, the
10 is an explanatory diagram showing the connection of intensity vectors of multiple channels in the trajectory
10 shows a case of connecting the intensity vectors of three channels and connects the
As described above, by using sound data collected by a plurality of sound collectors, the degree of separation in the identification space between vectors having different types of abnormalities is improved, and the diagnostic accuracy can be improved.
Fig. 11 is an explanatory diagram showing the effect of the abnormality diagnosis based on a locus vector obtained by connecting intensity vectors of multiple channels. Fig.
In FIG. 11A, the
In the first embodiment described above, the description has been given of the case where the
Since the overall configuration of the abnormal-sound diagnostic apparatus 100 according to the second embodiment is the same as that of the first embodiment, description of the block diagram will be omitted and the identification unit having a different configuration will be described in detail below.
12 is a diagram showing a configuration of an
The
Next, the operation of the
13 is a flowchart showing the operation of the abnormality diagnosis apparatus according to the second embodiment. Hereinafter, the same steps as those of the abnormality diagnosis apparatus according to the first embodiment will be assigned the same reference numerals as those used in Fig. 6, and the description thereof will be omitted or simplified. The operations of the
When the
Here, the inner product <φ (x1), φ ( x2)> between k (x 1, x 2) are mapped to a multi-dimensional space of vectors x1 φ (x 1) and the vector map of x2 in the multidimensional space of φ (x2) (Where φ (x) is a nonlinear function of vector x that can not be expressed by an explicit expression). As the kernel function, for example, a Gauss kernel expressed by the following equation (17) can be used. Also,? Is a parameter of the Gaussian kernel.
Next, the
As described above, even when the support vector machine is applied to the
In the first and second embodiments described above, the
In addition, the above-described lossless transformation shows that the vector representing the characteristic over the entire length in the time direction of the intensity time series 14 is not subjected to any processing, and the characteristic as it is is used as a vector. On the other hand, the conversion allowing the loss is a process for reducing the number of dimensions by multiplying, for example, a matrix obtained by principal component analysis, on a vector representing a characteristic over the entire length in the time direction of the intensity time series 14 And then the feature is used as a vector. It is considered that a part of the information included in the original feature vector is lost due to the reduction processing of the above-mentioned dimension number.
In the above-described first and second embodiments, when the device to be diagnosed is an elevator, the trajectory over the entire length in the time direction of the intensity time series is set as a vector However, it is also possible to divide the trajectory vector into a section for each one way such as a rising section and a falling section during the reciprocating operation of the moving section, and for each divided section, a trajectory over the entire length in the time direction of the section as a vector And the locus vector is extracted, and the identifying
As a result, in the case of an elevator, there is no abnormality at the time of rise, and even if there is an abnormality at the time of descent, diagnosis can be performed for each section.
Further, the segment to be divided may be divided not only into the rising section and the falling section but also the rising section, for example, into a more detailed section such as a lower section, a middle section, and a higher section.
(Industrial availability)
The abnormal sound diagnostic apparatus according to the present invention can be applied to an apparatus which can not generate a reference value for determining an abnormal sound for each individual because the abnormal sound diagnostic apparatus can perform the abnormal sound diagnosis with high accuracy with respect to the specification of the apparatus or the difference in operation, It is suitable for diagnosing the abnormal sound of the device.
1: collector part 2: waveform acquisition part
3: time frequency analysis unit 4: intensity time series acquisition unit
5: locus feature extraction unit 6: identification parameter storage unit
7, 7a: Identification part 8:
21: sound data generating unit 22: sound database
22a:
23: waveform acquisition unit 24: time frequency analysis unit
25: Strength Time Series Acquisition Unit 26: Trajectory Feature Extraction Unit
27: teacher vector creating unit 28: identification learning unit
71: input layer 72: first hiding layer
73: second hidden layer 100: abnormal sound diagnostic apparatus
100a, 200a:
200: identification parameter learning device
Claims (10)
A sound collecting part for collecting sounds generated in the diagnosis target device and acquiring sound data;
A strength time series acquisition unit for acquiring an intensity time series from a time frequency distribution obtained by analyzing waveform data of sound data acquired by the sound collector,
A trajectory feature extracting unit for extracting a trajectory vector by converting a trajectory indicative of an intensity characteristic in the entire time direction of the strength time series acquired by the strength time series acquisition unit into a vector,
A vector which is a trajectory showing a strength characteristic in the whole time direction of the intensity time series acquired from the time frequency distribution obtained by analyzing the waveform data of the sound data generated from the reference device is inputted and information indicating the state classification of the diagnosis target device An identification parameter storing section for storing the learned identification parameter as an output;
An identification unit that obtains a score for each state type of the diagnosis object device from the locus vector extracted by the locus feature extraction unit and the identification parameter stored in the identification parameter storage unit;
And a judging section for judging whether or not the sound generated in the diagnosis target device is normal or abnormal,
The abnormal sound diagnosis apparatus comprising:
Wherein the strength time series acquisition unit acquires the strength with respect to time and frequency from the time frequency distribution as the strength time series,
The trajectory feature extraction unit converts the locus indicated by the strength time series acquired by the strength time series acquisition unit into a vector in a two-dimensional space with respect to time and frequency, extracts the trajectory vector by connecting the converted vectors
Wherein the abnormal sound diagnostic apparatus comprises:
Wherein the trajectory characteristic extraction unit performs a lossless vector conversion on the intensity time series acquired by the strength time series acquisition unit or performs vector conversion of loss loss.
Wherein the identification unit acquires the score using a method of a neural network.
Wherein the identification unit acquires the score using a method of a support vector machine.
A plurality of sound collecting units for collecting sound data of a plurality of channels,
Wherein the strength time series acquisition section acquires the intensity time series of the plurality of channels from a time frequency distribution obtained by analyzing waveform data of each sound data of a plurality of channels collected by the collector section,
Wherein the trajectory feature extraction unit is configured to convert the locus indicating the intensity characteristic of the entire time direction of the intensity time series of the plurality of channels acquired by the strength time series acquisition unit into a vector, to connect the converted vectors of the plurality of channels in the time direction, To extract
Wherein the abnormal sound diagnostic apparatus comprises:
Wherein the diagnosis subject device travels in a movable section composed of a plurality of divided sections,
Wherein the trajectory feature extraction unit extracts the trajectory vector by converting a trajectory indicative of an intensity characteristic of the entire time direction of the strength time series to a vector for each divided section,
The identification unit performs identification processing for each division section
Wherein the abnormal sound diagnostic apparatus comprises:
Identification parameter learning device
And,
Wherein said identification parameter learning device comprises:
A sound database that stores sound data generated from the reference device, joint noise superimposition data in which sounds are superimposed on the sound data, and abnormality type information of a device associated with the sound data and the joint noise data,
A parameter strength time series acquisition unit for acquiring a strength time series from the time frequency distribution obtained by analyzing the waveform data of the sound data and the joint overlap data accumulated in the sound database,
A parameter trajectory feature extraction unit for converting, from a strength time series acquired by the parameter strength time series acquisition unit, a trajectory indicative of an intensity characteristic in the entire time direction of the strength time series to a vector,
A teacher vector creating unit that creates a teacher vector from the abnormal type information stored in the sound database;
Wherein the learning unit performs learning such that the trajectory vector converted by the parameter trajectory feature extraction unit is input so that the teacher vector generated by the teacher vector creation unit becomes an output and stores the result of the learning as an identification parameter of the abnormality diagnosis apparatus The identification learning unit
Wherein the abnormal sound diagnosis system comprises:
A step of collecting sound generated in the diagnostic target device by the collector part to acquire sound data,
A step of acquiring an intensity time series from a time frequency distribution obtained by analyzing waveform data of the sound data,
A step of extracting a trajectory vector by converting a trajectory characteristic section extracting section, which represents a characteristic feature of intensity in the entire time direction of the intensity time series, into a vector,
The identification unit is configured to receive, as an input, a vector which is a locus indicating a strength characteristic in the entire time direction of the intensity time series acquired from the time frequency distribution obtained by analyzing the waveform data of sound data generated from the reference device, A step of obtaining a score for each state type of the diagnostic target device from the trained vector,
The judgment section refers to the score, and judges whether or not the sound generated in the diagnosis target device is normal or abnormal,
Wherein the abnormal tone diagnosis method comprises the steps of:
A strength time series acquisition processing procedure for acquiring an intensity time series from a time frequency distribution obtained by analyzing the waveform data of the sound data,
A trajectory feature extraction processing procedure for extracting a trajectory vector by converting a trajectory showing the intensity characteristic of the entire time direction of the intensity time series to a vector,
A vector which is a trajectory showing a strength characteristic in the whole time direction of the intensity time series acquired from the time frequency distribution obtained by analyzing the waveform data of the sound data generated from the reference device is inputted and information indicating the state classification of the diagnosis target device An identification processing procedure for obtaining a score for each state type of the diagnostic target device from the learned identification parameter and the locus vector as an output,
Refers to the score, and judges whether or not the sound generated in the diagnostic target device is normal or abnormal,
Readable recording medium storing an abnormal sound diagnostic program for causing a computer to execute an abnormal sound diagnostic program.
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