CN113670609B

CN113670609B - Fault detection method, system, device and medium based on wolf optimization algorithm

Info

Publication number: CN113670609B
Application number: CN202110823433.7A
Authority: CN
Inventors: 岳夏; 王亚东; 张春良; 朱厚耀; 翁润庭; 陆凤清; 李植鑫
Original assignee: Guangzhou University
Current assignee: Guangzhou University
Priority date: 2021-07-21
Filing date: 2021-07-21
Publication date: 2022-10-04
Anticipated expiration: 2041-07-21
Also published as: CN113670609A

Abstract

The invention discloses a fault detection method, a system, a device and a medium based on a wolf optimization algorithm, wherein the method comprises the following steps: acquiring a first vibration signal of equipment to be detected, and decomposing the first vibration signal through a suffix tree algorithm to obtain a first repeated characteristic waveform and a first repeated time sequence; determining a first time-frequency characteristic diagram of the first vibration signal according to the first repeated characteristic waveform and the first repeated time sequence; optimizing the first time-frequency characteristic diagram through a grey wolf optimization algorithm, and extracting fault characteristic information in the first time-frequency characteristic diagram; and determining the fault type of the equipment to be detected according to the fault characteristic information. According to the invention, on one hand, errors caused by aliasing of a Fourier algorithm are avoided, and the accuracy of signal feature extraction is improved, and on the other hand, the time frequency feature map is optimized through a Hui wolf optimization algorithm to realize the extraction of fault feature information, so that the accuracy of equipment fault detection is improved. The invention can be widely applied to the technical field of fault detection.

Description

Fault detection method, system, device and medium based on wolf optimization algorithm

Technical Field

The invention relates to the technical field of fault detection, in particular to a fault detection method, a fault detection system, a fault detection device and a fault detection medium based on a wolf optimization algorithm.

Background

At present, most fault diagnosis and analysis of mechanical equipment are based on vibration signals, and the vibration signals have the characteristics of nonlinearity, non-stationarity and the like, and information which fully expresses signal characteristics can be obtained by using the vibration signals. In the prior art, for the processing of vibration signals, time-frequency conversion methods such as fourier transform are generally adopted, for example, a rolling bearing fault feature extraction method based on Daubechies wavelet transform. The method can generate errors due to aliasing phenomenon in the processing process, and the errors are generated by the Fourier algorithm principle and are inevitable. Due to the large error of feature extraction of the vibration signal, the existing fault detection method for the vibration signal is often inaccurate. In addition, for the imaging result after feature extraction, when the neural network is simply adopted for feature recognition, all information can be completely included depending on more network layers and neurons, the coupling relation between the neurons and the network layers cannot be judged, and meanwhile, the problems of over-fitting and under-fitting may exist, and the accuracy of fault detection is also influenced.

Disclosure of Invention

The present invention aims to solve at least to some extent one of the technical problems existing in the prior art.

Therefore, an object of an embodiment of the present invention is to provide a fault detection method based on a grey wolf optimization algorithm, which, on one hand, decomposes a vibration signal to obtain information of two different scales of a repeated characteristic waveform and a repeated time sequence, thereby avoiding an error caused by an aliasing phenomenon of a fourier algorithm and improving accuracy of signal characteristic extraction; on the other hand, the time-frequency characteristic diagram is optimized through the gray wolf optimization algorithm, and from the perspective of overall search, the extraction of fault characteristic information can be better completed, so that the accuracy of equipment fault detection is improved.

Another object of an embodiment of the present invention is to provide a fault detection system based on a grey wolf optimization algorithm.

In order to achieve the technical purpose, the technical scheme adopted by the embodiment of the invention comprises the following steps:

in a first aspect, an embodiment of the present invention provides a fault detection method based on a grayish wolf optimization algorithm, including the following steps:

acquiring a first vibration signal of equipment to be detected, and decomposing the first vibration signal through a suffix tree algorithm to obtain a first repeated characteristic waveform and a first repeated time sequence;

determining a first time-frequency signature of the first vibration signal from the first repeating signature and the first repeating time series;

optimizing the first time-frequency characteristic diagram through a grey wolf optimization algorithm, and extracting fault characteristic information in the first time-frequency characteristic diagram;

and determining the fault type of the equipment to be detected according to the fault characteristic information.

Further, in an embodiment of the present invention, the step of decomposing the first vibration signal by using a suffix tree algorithm to obtain a first repeating characteristic waveform and a first repeating time sequence specifically includes:

coding the first vibration signal through average distribution or Gaussian distribution to obtain a first time domain signal;

decomposing the first time domain signal through a suffix tree algorithm to obtain a plurality of fault waveform information and corresponding time information, and constructing a first suffix tree according to the fault waveform information and the time information;

and traversing each node of the first suffix tree, acquiring repeated fault waveform information as a first repeated characteristic waveform, and determining a first repeated time sequence of the first repeated characteristic waveform.

Further, in an embodiment of the present invention, the step of traversing each node of the first suffix tree, acquiring repeatedly occurring fault waveform information as a first repeated signature, and determining a first repeated time sequence of the first repeated signature specifically includes:

traversing each node of the first suffix tree by a depth-first nested traversal algorithm from a root node of the first suffix tree;

acquiring repeated fault waveform information as a first repeated characteristic waveform, and determining a plurality of time information corresponding to the first repeated characteristic waveform;

and determining a first repeating time sequence of the first repeating characteristic waveform according to a plurality of time information corresponding to the first repeating characteristic waveform.

Further, in an embodiment of the present invention, the step of determining the first time-frequency characteristic diagram of the first vibration signal according to the first repeating characteristic waveform and the first repeating time sequence specifically includes:

and normalizing the first repeated characteristic waveform and the first repeated time sequence according to a preset repeated length range to obtain a first time-frequency characteristic diagram of the first vibration signal.

Further, in an embodiment of the present invention, the step of performing optimization processing on the first time-frequency feature map through a grey wolf optimization algorithm to extract fault feature information in the first time-frequency feature map specifically includes:

determining an initial wolf population according to the first time-frequency characteristic diagram;

calculating the fitness of each grey wolf individual in the initial grey wolf population, and further determining a first grey wolf individual, a second grey wolf individual and a third grey wolf individual according to the fitness;

iteratively updating the initial grey wolf population according to the first grey wolf individual, the second grey wolf individual and the third grey wolf individual, and determining a hunting target position according to the position of each current grey wolf individual when a preset iteration number is reached;

and determining the fault characteristic information in the first time-frequency characteristic diagram according to the hunting target position.

Further, in an embodiment of the present invention, the step of iteratively updating the initial population of grey wolves according to the first individual of grey wolves, the second individual of grey wolves and the third individual of grey wolves specifically includes:

updating the position of each individual grey wolf according to the positions of the first individual grey wolf, the second individual grey wolf and the third individual grey wolf;

and recalculating the fitness of each grey wolf individual according to the updated position of each grey wolf individual, and re-determining the first grey wolf individual, the second grey wolf individual and the third grey wolf individual.

Further, in an embodiment of the present invention, the step of determining the fault type of the device to be tested according to the fault characteristic information specifically includes:

and inputting the fault characteristic information into a pre-trained fault characteristic recognition model to obtain the fault type of the equipment to be detected.

In a second aspect, an embodiment of the present invention provides a fault detection system based on a grayling optimization algorithm, including:

the signal decomposition module is used for acquiring a first vibration signal of the equipment to be detected and decomposing the first vibration signal through a suffix tree algorithm to obtain a first repeated characteristic waveform and a first repeated time sequence;

a time-frequency characteristic diagram determining module, configured to determine a first time-frequency characteristic diagram of the first vibration signal according to the first repeating characteristic waveform and the first repeating time sequence;

the fault characteristic information extraction module is used for optimizing the first time-frequency characteristic diagram through a wolf optimization algorithm and extracting fault characteristic information in the first time-frequency characteristic diagram;

and the fault type determining module is used for determining the fault type of the equipment to be detected according to the fault characteristic information.

In a third aspect, an embodiment of the present invention provides a fault detection apparatus based on a grayish wolf optimization algorithm, including:

at least one processor;

at least one memory for storing at least one program;

the at least one program, when executed by the at least one processor, causes the at least one processor to implement a fault detection method based on a wolf optimization algorithm as described above.

In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, in which a program executable by a processor is stored, where the program executable by the processor is used to execute the fault detection method based on the graying optimization algorithm.

Advantages and benefits of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention:

according to the embodiment of the invention, the first vibration signal of the equipment to be detected is obtained, the first vibration signal is decomposed through a suffix tree algorithm to obtain a first repeated characteristic waveform and a first repeated time sequence, then the first time-frequency characteristic diagram is determined according to the first repeated characteristic waveform and the first repeated time sequence, and then the fault characteristic information in the first time-frequency characteristic diagram is extracted through a Hui wolf optimization algorithm, so that the fault type of the equipment to be detected can be determined according to the fault characteristic information. On one hand, the embodiment of the invention obtains the information of two different scales of the repeated characteristic waveform and the repeated time sequence by decomposing the vibration signal, avoids the error generated by the aliasing phenomenon of the Fourier algorithm and improves the accuracy of signal characteristic extraction; on the other hand, the time-frequency characteristic diagram is optimized through the gray wolf optimization algorithm, and from the perspective of overall search, the extraction of fault characteristic information can be better completed, so that the accuracy of equipment fault detection is improved.

Drawings

In order to more clearly illustrate the technical solution in the embodiment of the present invention, the following description is made on the drawings required to be used in the embodiment of the present invention, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solution of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.

Fig. 1 is a flowchart illustrating steps of a fault detection method based on a gray wolf optimization algorithm according to an embodiment of the present invention;

FIG. 2 is an exploded view of a suffix tree algorithm provided by an embodiment of the present invention;

FIG. 3 is a schematic diagram illustrating a leaf node recursive call sequence according to an embodiment of the present invention;

fig. 4 is a schematic diagram of time-frequency characteristics provided in an embodiment of the present invention;

FIG. 5 is a schematic diagram of the relationship between the individual gray wolf and the prey in the gray wolf optimization algorithm provided by the embodiment of the present invention;

FIG. 6 is a schematic diagram of a hunting process of the Greenwolf optimization algorithm according to an embodiment of the present invention;

fig. 7 is a block diagram of a fault detection system based on a grayish wolf optimization algorithm according to an embodiment of the present invention;

fig. 8 is a block diagram of a fault detection apparatus based on a grayish wolf optimization algorithm according to an embodiment of the present invention.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.

In the description of the present invention, the meaning of a plurality is two or more, if there is a description to the first and the second for the purpose of distinguishing technical features, it is not understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or implicitly indicating the precedence of the indicated technical features. Furthermore, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.

Referring to fig. 1, an embodiment of the present invention provides a fault detection method based on a grey wolf optimization algorithm, which specifically includes the following steps:

s101, obtaining a first vibration signal of equipment to be detected, and decomposing the first vibration signal through a suffix tree algorithm to obtain a first repeated characteristic waveform and a first repeated time sequence;

specifically, a sensor is used for collecting a first vibration signal f (n) of equipment to be detected, and n is the number of sampling points. The data size of a single process requires a complete cycle including at least 2 signals of interest, which should generally be more than 3-5 times the cycle of the features of interest, in order to obtain more fault repetition features.

As a further optional implementation, the step of decomposing the first vibration signal by using a suffix tree algorithm to obtain a first repeating characteristic waveform and a first repeating time sequence specifically includes:

a1, coding a first vibration signal through average distribution or Gaussian distribution to obtain a first time domain signal;

a2, decomposing the first time domain signal through a suffix tree algorithm to obtain a plurality of fault waveform information and corresponding time information, and constructing a first suffix tree according to the fault waveform information and the time information;

and A3, traversing each node of the first suffix tree, acquiring repeated fault waveform information as a first repeated characteristic waveform, and determining a first repeated time sequence of the first repeated characteristic waveform.

Specifically, the first vibration signal is encoded by a preset number of encoding bits in accordance with an average distribution or a gaussian distribution in accordance with an amplitude section of the first vibration signal.

The formula for encoding the first vibration signal in terms of the average distribution is as follows:

C ₁ (n)＝Int[(f(n)-f _1，min )*L _code /(f _1，max -f _1，min )]

the formula for encoding the first vibration signal according to a gaussian distribution is as follows:

C ₁ (n)＝Int[IGD((f(n)-μ ₁ )/σ ₁ )*L _code ]

wherein, C ₁ (n) is the encoded value of the first code band corresponding to the n times of sampled data, i.e. the first time domain signal, int () is a rounding function, L _code To a predetermined number of coded bits, f _1,max Is the maximum value of the value range of the first vibration signal, i.e. the maximum value of the value range when the first code band is encoded, f _1,min Is as followsA value domain minimum value of the vibration signal, namely the value domain minimum value when the first code band is coded; mu.s ₁ Is the mean value of Gaussian distribution of the first code band, sigma ₁ The IGD is a standard normal distribution integral probability look-up table return function for the gaussian distribution variance of the first code band.

Obtaining the residual signal R simultaneously ₁ (n)：R ₁ (n)＝f(n)-C ₁ (n)。

C after coding ₁ And (n) performing suffix tree reconstruction. Three delivery rules are followed in the suffix tree construction process:

relu1: used when inserting a new suffix to the root node root. active _ node remains root, active _ edge is set to the first character of the new suffix to be inserted, active _ length is decremented by 1.

Relu2: when an edge is Split (Split) and a new node is inserted (Insert), if the new node is not the first node created in the current step, the previously inserted node is connected to the new node by a special pointer, called Suffix Link (Suffix Link), which is usually drawn with a dashed line in the illustration.

Relu3: when the active _ node is not a node splitting edge of the root node root, searching a node along the direction of Suffix connection (Suffix Link), and if one node exists, setting the node as the active _ node; if not, active _ node is set to root. active _ edge and active _ length remain unchanged.

The suffix tree algorithm employed in the embodiments of the present invention is described below. There is one raw data column T = T ₁ t ₂ ...t _n Wherein t is _i (1. Ltoreq. I.ltoreq.n + 1), n being the data length, from t ₁ To t _n The original data is sequentially decomposed into n +1 non-repetitive subsequences, and the n +1 th subsequence is a specified terminator and is indicated by '#'. For ease of expression, the relevant symbols are illustrated below:

o: (root) root node, sequence starting point, has no specific meaning;

p: (acitve _ piont) an activity point, specifying an activity starting point;

n: (active _ node) an active node designating a child node;

e: (active _ edge) active edge, specifying sequence connection direction;

l: (active _ edge) active length, specifying the amount of data moved by the sequence;

r: (remaining) number of remaining suffixes, indicating number of unconnected suffixes;

#: a terminator;

street (T): and finally decomposing the result.

The original data sequence is decomposed from left to right in order starting from the root node O until the (n + 1) th sequence is generated. The following formula:

STree(T)＝(F _i ,f _i ,g _i ),i∈[1,n]

wherein, F _i Representing the sequence of the main edge, f _i Denotes a sub-edge sequence, g _i And the connection mode of the data i is represented and comprises the values of the activity point P and the residual suffix R. When the data is at t _i And (i is more than or equal to 1 and less than or equal to n), completing the connection of each edge in the following transmission mode.

1) When i =1

P ₁ ＝(O,'F ₁ ', 1), R =1. Selecting a root node O from the initial position; the movable edge E is set as' F ₁ '; the active length L and the number of remaining suffixes R are set to 1, indicating that only one amount of data needs to be transferred in. STRee (T) ¹ )＝(F ₁ ,g ₁ )。

2) When i >1

①

I.e. t _i Is STRee (T) ^i-1 ) Newly appearing data later, setting P _i ＝(O,'F ₁ ', i), R = i. Directly connected after all the main edges, slave F ₁ Starting the iterative update P _ik ＝(O,'F _k ', i-k + 1) where k represents the number of primary edges, k is an element (0,i)]K = k +1, r = r-1, until i = k, stopping updating P _ik . After extending the existing edge, a new main sequence F is created ^k+1 Starting from the root node O, t _i As its first side. STRee (T) ⁱ )＝(F _i ,g _i )。

②t _i ∈T ^i-1 I.e. t _i Already present in the prefix, is considered duplicate data. From the main chain F ₁ First data t of ₀ Start, find and t _i Taking j as the position where the repeated data appears represents the side length L. Setting P _i ＝(O,'F ₁ ', j), R =1. Due to unknown t _i+1 Data characteristic of (2) that temporarily only the fixed movement point P can be taken _i And the number of remaining suffixes R, do not give a specific direction of the data sequence. J can tell that all the first j backbones are present with t _i Repeated data, so that j starts at 1, updating P iteratively _ij ＝(O,'F _j ',i-j)，L∈(0,j]L = L-1, R =1, the primary edge continues to be t in each update _i R value is constant and 1, only for t _i This one data volume operates. In the step, the rule 1 can be referred to, the active point is a root node, the active edge is set as the initial data of the new suffix, and after one operation, the active edge length is reduced by 1. The process does not create a new edge. The update of the active point is used to concatenate suffix data and cannot be used as a standard for creating new edges.

(3) On the premise of step (2), creating a subsequence f _i ，f _i All with a moving point P _ij As a starting point, from the main edge F _j Middle separation, following main edge F _j The prefix data of (c). Subsequence f _i In-lead data with primary edge F _j Match, data t _i And splicing after the sequence.

STree(T ⁱ )＝(F _i ,f _i ,g _i ),i＜n。

(4) A Suffix connection (Suffix Link) is established. Repeating data t _i When present, each main edge generates one or more sub-edge nodes P _Fi If the node is not the first node P created in the current data insertion process _F1 The previously inserted node is connected to the new node by a special pointer, called a suffix connection.

(5) When the node from N is not a node split edge of the root node O, searching a node along the direction of Suffix connection (Suffix Link), and if one node exists, setting the node as N; if not, N is set to O. E and L remain unchanged.

(6) And (5) circulating the steps (2) to (5) until i = n, and completing the decomposition of all data.

Taking the 'abcabxabcd' character string as an example, suffix tree decomposition of the character string is completed, as shown in fig. 2, the serial numbers are the numbers of the main and sub edges, '#' is an end sign, the open circles represent parent nodes, the triangle numbers represent leaf nodes, and the arrows represent the search order.

After adding the terminator, a total of 11 substrings are divided as shown in table 1 below:

TABLE 1

It is clear from table 1 that the original character string can be decomposed into several non-repetitive substrings, and the information amount carried by the substrings is different. And (4) postfix tree decomposition, namely processing original data, finishing the rearrangement process of data coding, replacing the reading time of the characteristic data segment by sacrificing memory space, and completely reserving all data signals.

As a further optional implementation, the step of traversing each node of the first suffix tree, acquiring repeatedly-occurring fault waveform information as the first repeated characteristic waveform, and determining a first repeated time sequence of the first repeated characteristic waveform specifically includes:

a31, traversing each node of the first suffix tree by a depth-first nested traversal algorithm from the root node of the first suffix tree;

a32, acquiring repeated fault waveform information as a first repeated characteristic waveform, and determining a plurality of time information corresponding to the first repeated characteristic waveform;

a33, determining a first repeating time series of the first repeating characteristic waveform according to the plurality of time information corresponding to the first repeating characteristic waveform.

As a further optional implementation manner, the step of traversing each node of the first suffix tree by using a depth-first nested traversal algorithm specifically includes:

b1, creating a repeated time storage array with the same length as the first time domain signal;

b2, creating a repeated feature record array consistent with the number of the non-leaf nodes of the first suffix tree;

b3, running a Depth _ First nested function, inputting a node number and a father node repeated waveform length, and outputting a repeated moment initial position, a repeated waveform length, a waveform repetition number, a node repeated character string length and a plurality of repeated moments;

the repeated characteristic recording array is used for storing the repeated moment starting position, the repeated waveform length, the waveform repetition times and the node repeated character string length, and the repeated moment storage array is used for storing the repeated moment.

Specifically, the algorithm pseudo-code is as follows:

depth _ First nested function (abbreviated DF):

inputting: node number nNodeID, father node repeating waveform length nFatherNodeRepeatLength;

and (3) outputting: the method comprises the steps of (1) starting position of a repetition moment, length nNodeRepeatLength of a repetition waveform, waveform repetition times nWRindex, and a repetition moment storage array (global variable);

1: repeated waveform length = current node character length + parent node repeated waveform length

2: recording the starting position of the node repetition time

3: accessing a first child node of the node according to the node number

4: number of waveform repetitions =0

5：repeat

6: if child node is a non-leaf node

7: recursively calling a child node DF function to obtain the waveform repetition times of the child node

8: waveform repetition number = waveform repetition number + child node waveform repetition number

9: else// child node is a leaf node

10: waveform repetition number = waveform repetition number +1

11: filling leaf node numbers into a repeat time storage array

12：end if

13: all child nodes of the unitil have access to

14: return repeated waveform length

Depth-first algorithm:

1: creating a repeated time storage array with the same length as the signal to be processed;

2: creating an array of duplicate feature records consistent with the number of non-leaf nodes, containing the following information: { repeating waveform string termination address, node repeating string length, repeating time starting position, waveform repeating times };

3: running DF (root node number, 0)// father node repeating waveform length is 0;

4: and obtaining the characteristic waveform of the fault and the characteristic of the corresponding repeated time sequence.

The time-frequency feature extraction algorithm only traverses the nodes created in the suffix tree algorithm once. Since the suffix tree algorithm has a complexity of O (n), the time-frequency feature extraction algorithm also has a complexity of O (n).

The basic principle of the time-frequency feature extraction algorithm in the embodiment of the invention is further described below by taking the suffix tree structure of the abcabxabcd character string as an example.

The recursive calling sequence of the DF function is shown in FIG. 3, and is DF (0,0), DF (1,0), DF (3,2), DF (4,1), DF (2,0), DF (5,0) and DF (0,0) in turn, and in each main edge, the leaf node which has been visited needs to return to the parent node to wait for the next node search.

The subsequence feature array is completed according to the order of the non-leaf nodes in fig. 2, and the decomposition results are shown in table 2 and table 3.

1	2	3	4	5	6	7	8	9	10	11
											a	b	c	a	b	x	a	b	c	d	#
1	7	4	2	8	5	3	9	6	10	11

TABLE 2

Table 2 the 1 st behavior primitive character string 'abcabxabcd' each character number, the 2 nd behavior primitive character string, and the 3 rd behavior extracted repetitive feature decomposition sequence, whose value corresponds to the leaf node number in fig. 2 and also to the time of the sampling point indicated by the value. The storage capacity of the array is consistent with the length of the original data, the last bit of the array always corresponds to the character string terminator, and the bit can be omitted.

NUM	W_End	ReLen	Start_RTV	RepTimes	Node_str
							1	2	2	1	3	ab
2	2	1	4	3	b
						3	3	3	1	2	c
4	3	2	4	2	c
						5	3	1	7	2	c

TABLE 3

Table 3 records the repetitive feature array information of each non-leaf node, including the repetitive feature waveform end point, the repetitive feature waveform length ReLen, the repetitive time feature vector Start point Start _ RTV, and the repetition times RepTimes. For easy understanding, the Node number NUM and the Node string Node _ str are added in table 3. The node numbers and the node character strings in table 3 are all the same as the labels in fig. 2, and can be directly obtained by a suffix tree algorithm.

As a further optional implementation, the fault detection method further includes the following steps:

and when the maximum repetition length is greater than or equal to the preset length threshold and the residual coding frequency is less than or equal to the preset coding frequency threshold, updating the parameters of the residual coding and then carrying out residual coding and signal decomposition.

Specifically, the maximum repetition length of the waveform exceeding a preset repetition number is checked, and the analysis is terminated if the length is smaller than a preset value or the residual analysis number i is larger than a preset value. If the length is greater than or equal to the preset value and the residual error analysis time i is less than or equal to the preset value, the ith residual error signal R is processed _i (n) continuing to perform the decomposition.

The rule of parameter modification in residual coding is as follows:

if the encoding is performed according to the average distribution, the upper and lower limits are updated using the following formula:

if the encoding is performed according to a gaussian distribution:

a) Residual coding can be performed again according to the average distribution, and the upper limit and the lower limit are updated by adopting the formula.

b) Still according to the gaussian distribution encoding, the parameters are updated using the following formula:

μ _i ＝0

and then residual error coding is carried out by using the following formula:

C _i (n)＝Int[(IGD((R _i-1 (n)-μ _i )/σ _i )-0.5)*2*L _code ]

and S102, determining a first time-frequency characteristic diagram of the first vibration signal according to the first repeated characteristic waveform and the first repeated time sequence.

Specifically, the output fault signature contains two pieces of information, namely a repetitive signature waveform and a repetitive time series. As shown in table 3, the length ReLen of the repetitive characteristic waveform is at most 3, and the End point W _ End of the corresponding repetitive characteristic waveform is 3, so the waveform of the characteristic is "abc" shown in the first 3 bits of row 2 in table 2, and the corresponding repetitive time series characteristic is 2 sequences of the total number of repetitions RepTimes from the Start point Start _ RTV, i.e., the 1 st bit, of the repetitive time characteristic vector in row 3 in table 2, i.e., "1,7". Therefore, the fault characteristics of a sampling point with a repetitive waveform length of 3 are as follows:

{“abc”，“1,7”}

similarly, the repetition length of 2 is characterized by:

{“ab”，“1,7,4”}、{“bc”，“2,8”}

the repetitive signature and repetitive time series may be used individually or collectively as a fault signature for subsequent fault diagnosis. And by combining a dynamic model, the set operation results of intersection, union set, complement set and the like of a plurality of repeated time sequences can be used as fault characteristics. Wherein a longer maximum waveform repetition length at a time instant means a better stability of the data structure for a longer time around the time instant. When the interference is approximately white, the instantaneous frequency at that instant is also relatively low. Conversely, a shorter maximum waveform repetition length at a time point means that the data structure at that time point is less stable and the transient signal at that time point is closer to the transient or impulsive signal.

As a further optional real-time mode, a first time-frequency feature map of the first vibration signal is determined according to the first repeating feature waveform and the first repeating time sequence, and a training sample set is constructed according to the first time-frequency feature map, which specifically includes:

Specifically, the abscissa of the first time-frequency characteristic diagram represents time information, the ordinate of the first time-frequency characteristic diagram represents a repeated waveform length, and the color value of the pixel point of the first time-frequency characteristic diagram represents the participation degree of data at the corresponding time in the repeated characteristic waveform of the corresponding repeated waveform length, so that the fault feature visualization of the first vibration signal can be realized.

Fig. 4 is a schematic diagram of time-frequency characteristics provided in the embodiment of the present invention. The string data is analogized to the vibration signal, and the shades in fig. 4 represent the activity of the data in the current repetition length at different times. The corresponding color value of the line 2 "b" character is lighter, indicating that there is likely to be a periodic signal of lower frequency. The numerical values of the characters of 'x' and'd' on the 1 st line have low values, which indicates that the signal at the moment is extremely unstable, and abrupt signals such as impact can exist. The numerical value of the character a at the 1 st row and the 2 nd row also has a low value, but the corresponding position value of the second row is larger, which indicates that the corresponding signal of the character a belongs to the subsequent character ab, and the character a does not depart from the character ab in the processed data and appears independently.

And after the suffix tree is decomposed, obtaining an image result with time-frequency characteristics. The position of a low-frequency pit appearing in the image can be regarded as the moment when the fault piece is impacted; the high frequency highlight areas appearing in the image can be considered as the natural vibration signals of the system. For a rotating system, the rotating period is small, the vibration frequency is high, and it is difficult to directly judge the difference between color blocks of an image (the color blocks are characterized as signal time-frequency characteristics), so the embodiment of the invention introduces a gray wolf optimization algorithm to extract the area where the fault characteristic information in the time-frequency characteristic diagram is located, thereby accurately judging the specific fault type.

S103, optimizing the first time-frequency characteristic diagram through a grey wolf optimization algorithm, and extracting fault characteristic information in the first time-frequency characteristic diagram.

Specifically, the Grey Wolf optimization algorithm is a group intelligent optimization algorithm (Grey Wolf Optimizer, GWO) proposed in 2014 by scholars of university of griffis, australia, and the related feature extraction and optimization are performed by simulating hunting activities of wolves prey, so that the Grey Wolf optimization algorithm has the characteristics of strong convergence performance, few parameters, easiness in implementation and the like. Step S103 specifically includes the following steps:

s1031, determining an initial wolf population according to the first time-frequency characteristic diagram;

s1032, calculating the fitness of each grey wolf individual in the initial grey wolf population, and further determining a first grey wolf individual, a second grey wolf individual and a third grey wolf individual according to the fitness;

s1033, carrying out iterative updating on the initial gray wolf population according to the first gray wolf individual, the second gray wolf individual and the third gray wolf individual, and determining a hunting target position according to the positions of the current gray wolf individuals when preset iteration times are reached;

s1034, determining fault characteristic information in the first time-frequency characteristic diagram according to the hunting target position.

As a further optional implementation, the step of iteratively updating the initial population of sirius based on the first individual of sirius, the second individual of sirius and the third individual of sirius specifically includes:

c1, updating the position of each grey wolf individual according to the positions of the first grey wolf individual, the second grey wolf individual and the third grey wolf individual;

and C2, recalculating the fitness of each grey wolf individual according to the updated position of each grey wolf individual, and re-determining the first grey wolf individual, the second grey wolf individual and the third grey wolf individual.

Specifically, the optimization process of GWO is a simulated wolf hunting process, which involves drawing up the members of each level of wolf and coordinating the specific division of labor, and includes the social level layering, tracking, surrounding and attacking of wolf, and the process is as follows:

when the GWO optimization model is constructed, a gray wolf social level hierarchical model needs to be constructed first. Calculating the fitness of each individual of the population, and sequentially marking the three wolfsbane with the best fitness in the wolfsbane population as the three wolfsbane

(i.e., the first individual grey wolf), β (i.e., the second individual grey wolf), δ (i.e., the third individual grey wolf), and the remaining grey wolfs are labeled ω. That is, the social ranks in the wolf group are ranked from high to low as:

β, δ, ω. GWO optimization procedure consists essentially of the optimal three solutions in each generation of population (i.e., the optimal three solutions

β, δ) to guide completion.

The gray wolf individual searching for the prey gradually approaches the prey and surrounds it, and the mathematical model of the behavior is as follows:

wherein t is the current iteration times, and the vector product adopts hadamard product operation;

and

is a co-ordinate coefficient vector; xp represents the location vector of the prey (i.e., hunting target); x (t) represents the position vector of the current gray wolf; vector of co-ordinated coefficients

And

the calculation formula of (a) is as follows:

a is linearly reduced to 0 from 2 in the whole iteration process; r1 and r2 are random vectors in [0,1 ].

And

the change of the vector determines the position relationship between the gray wolf and the prey in two-dimensional coordinates, as shown in fig. 5, the initial position of the gray wolf is located at (x, y), and the prey is located at the center of the possible point. Adjusted by decision maker

And

the vector changes the position of the gray wolf to update the relative position relationship between the gray wolf and the prey. For example to set

And

to change the gray wolf position point to (x-x, y).

The gray wolf has the ability to identify the location of potential prey (i.e., a hunting target, i.e., an optimal solution), and the search process relies primarily on

Beta, delta gray wolf. But the solution space characteristics of many problems are unknown and the grayish wolf is an inability to determine the exact location of the prey (optimal solution). To simulate the search behavior of the wolf, assume

Beta, delta has a strong ability to identify potential prey locations. Thus, during each iteration, the best three wolfberries in the current population are retained(s) ((s))

β, δ) and then according to their bitThe position information updates the position of other individual wolfs (including ω). The mathematical model of this behavior can be expressed as follows:

in the formula:

X _β ,X _δ respectively representing the position vectors of alpha, beta and delta in the current population; x represents a location vector of the gray wolf;

D _β ,D _δ respectively representing the distances between the current candidate gray wolf and the optimal three wolfs; when | A |>1, the gray wolves are scattered in each area as much as possible and search for the prey. When | a | < 1, the gray wolf will search a game of a certain area or a certain area in the centralized child.

As shown in fig. 6, which is a schematic diagram of the hunting process of the sirius optimization algorithm, it can be seen from fig. 6 that the positions of the solution candidates finally fall within the random circle positions defined by α, β, δ. In general, α, β, δ need to first predict the approximate location of the prey (potentially optimal solution), and then other wolf candidates update their locations randomly around the prey under the direction of the currently optimal wolf.

In the process of constructing the attack prey model, the reduction of the value a causes the value of A to fluctuate according to the mathematical model surrounding the prey. In other words, A is a random vector over the interval [ -a, a ], where a falls linearly during the iteration. When A is on the [ -1,1] interval, then the next moment location of the wolf individual (Search Agent) can be anywhere between the current wolf and the game.

The gray wolf mainly relies on information of alpha, beta, delta to search for prey. They begin to search for prey location information decentralised and then focus on attacking prey. For the creation of the distributed model, the search method enables GWO to perform a global search by making its searching agent remote from the prey by | a | > 1. Another search coefficient in the GWO algorithm is C. According to the mathematical model surrounding the prey, the C vector is a vector formed by random values in the interval range [0,2], and the coefficient provides random weight for the prey so as to increase (| C | > 1) or decrease (| C | < 1). This helps GWO exhibit random search behavior during optimization to avoid the algorithm from falling into local optima. It is worth noting that C does not decrease linearly, C is a random value in the iteration process, and the coefficient is beneficial for the algorithm to jump out of a local part, and especially, the algorithm is important in the later stage of the iteration.

In general, in the course of searching for the wolf pack, a random position of the wolf is given first, with iterative feedback, α, β, δ monitor the position where the optimal solution may appear, each monitor updates the distance between them and the optimal solution, and continuously iteratively optimizes in the course of reducing the parameter a from 2 to 0, and at the same time, the monitor judges whether the action of the wolf pack is far away from or close to the hunting object, and finally, the wolf optimization algorithm outputs the final hunting target position. The area of the specific fault feature can be determined in the first time frequency feature map according to the hunting target position, so that the fault feature signal can be extracted.

And S104, determining the fault type of the equipment to be detected according to the fault characteristic information.

Further as an optional implementation manner, the step of determining the fault type of the device to be detected according to the fault characteristic information specifically includes:

Specifically, after the fault feature information is extracted from the first time-frequency feature map, the fault feature information can be directly input into a fault feature recognition model trained in advance, so that the fault type can be recognized. The fault feature recognition model can be obtained by training fault feature information of a vibration signal with a known fault type by using a neural network, and the training process is not the key point of the invention and is not repeated herein.

It can be understood that, because the fault feature information is extracted from the first time-frequency feature map by the wolf optimization algorithm, compared with the method for directly carrying out model identification on the first time-frequency feature map, the method and the device for identifying the fault feature information can reduce the complexity and the training time of the identification model and improve the accuracy of fault identification.

The method steps of the present invention are described above. On one hand, the embodiment of the invention obtains the information of two different scales of the repeated characteristic waveform and the repeated time sequence by decomposing the vibration signal, avoids the error generated by the aliasing phenomenon of the Fourier algorithm and improves the accuracy of signal characteristic extraction; on the other hand, the time-frequency characteristic diagram is optimized through the gray wolf optimization algorithm, and from the perspective of overall search, the extraction of fault characteristic information can be better completed, so that the accuracy of equipment fault detection is improved.

Compared with the prior art, the embodiment of the invention also has the following advantages:

1) The suffix tree algorithm has real-time property in extracting fault signals. The storage capacity of the signals is moderately increased to achieve the purpose of rapidly extracting the repeated fault signals, and the millisecond level can be achieved under the condition that the hardware condition is met.

2) The integrity of the signal. The whole process of extracting the characteristics of the collected time domain signals has no signal loss, and all information of the concerned signals is reserved.

3) The automation degree is high. The processes of signal acquisition, processing, feature extraction, category identification and the like can be completed on line in sequence, and the aims of reducing the labor intensity and labor cost are fulfilled.

4) The universality is strong. The mutual relation between each decision maker and each characteristic is judged by the idea of integral search, the coupling relation between fault characteristics is kept to a greater extent, and the integral characteristics of fault categories can be transferred to other mechanical equipment or a test system and still have better action and effect.

5) Compared with a complex intelligent identification method, the fault identification method based on the wolf optimization algorithm is relatively simple, few in parameter value, high in training speed and strong in pertinence.

Referring to fig. 7, an embodiment of the present invention provides a fault detection system based on a grayish wolf optimization algorithm, including:

the time-frequency characteristic diagram determining module is used for determining a first time-frequency characteristic diagram of the first vibration signal according to the first repeated characteristic waveform and the first repeated time sequence;

The contents in the above method embodiments are all applicable to the present system embodiment, the functions specifically implemented by the present system embodiment are the same as those in the above method embodiment, and the beneficial effects achieved by the present system embodiment are also the same as those achieved by the above method embodiment.

Referring to fig. 8, an embodiment of the present invention provides a fault detection apparatus based on a grayish wolf optimization algorithm, including:

at least one processor;

at least one memory for storing at least one program;

when executed by the at least one processor, the at least one program causes the at least one processor to implement a fault detection method based on a grayish optimization algorithm as described above.

The contents in the above method embodiments are all applicable to the present apparatus embodiment, the functions specifically implemented by the present apparatus embodiment are the same as those in the above method embodiments, and the advantageous effects achieved by the present apparatus embodiment are also the same as those achieved by the above method embodiments.

Embodiments of the present invention also provide a computer-readable storage medium, in which a processor-executable program is stored, and the processor-executable program is used for executing the fault detection method based on the gray wolf optimization algorithm when being executed by a processor.

The computer-readable storage medium of the embodiment of the invention can execute the fault detection method based on the gray wolf optimization algorithm provided by the embodiment of the method of the invention, can execute any combination implementation steps of the embodiment of the method, and has corresponding functions and beneficial effects of the method.

Embodiments of the present invention also disclose a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and executed by the processor to cause the computer device to perform the method illustrated in fig. 1.

In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.

Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the above-described functions and/or features may be integrated in a single physical device and/or software module, or one or more of the functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.

The above functions, if implemented in the form of software functional units and sold or used as a separate product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the above method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (ram), a magnetic disk or an optical disk, and other various media capable of storing program codes.

The logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Further, the computer readable medium could even be paper or another suitable medium upon which the above described program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.

In the foregoing description of the specification, reference to the description of "one embodiment/example," "another embodiment/example," or "certain embodiments/examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims

1. A fault detection method based on a gray wolf optimization algorithm is characterized by comprising the following steps:

determining the fault type of the equipment to be detected according to the fault characteristic information;

the step of decomposing the first vibration signal by a suffix tree algorithm to obtain a first repetitive signature and a first repetitive time sequence specifically includes:

2. The fault detection method based on the grayling optimization algorithm according to claim 1, wherein the step of traversing each node of the first suffix tree, obtaining repeated fault waveform information as a first repeated signature, and determining a first repeated time sequence of the first repeated signature specifically includes:

3. The fault detection method based on the graying optimization algorithm according to claim 1, wherein the step of determining the first time-frequency characteristic diagram of the first vibration signal according to the first repeating characteristic waveform and the first repeating time sequence specifically comprises:

4. The fault detection method based on the grayling optimization algorithm according to claim 1, wherein the step of performing optimization processing on the first time-frequency feature map through the grayling optimization algorithm to extract fault feature information in the first time-frequency feature map specifically comprises:

5. The fault detection method based on the grayling optimization algorithm according to claim 4, wherein the step of iteratively updating the initial grayling population according to the first grayling individual, the second grayling individual and the third grayling individual specifically comprises:

and recalculating the fitness of each individual gray wolf according to the updated position of each individual gray wolf, and re-determining the first individual gray wolf, the second individual gray wolf and the third individual gray wolf.

6. The fault detection method based on the graying optimization algorithm according to any one of claims 1 to 5, wherein the step of determining the fault type of the device to be detected according to the fault characteristic information specifically comprises:

7. A fault detection system based on a grey wolf optimization algorithm, comprising:

the fault type determining module is used for determining the fault type of the equipment to be detected according to the fault characteristic information;

traversing each node of the first suffix tree, acquiring repeated fault waveform information as a first repeated characteristic waveform, and determining a first repeated time sequence of the first repeated characteristic waveform.

8. A fault detection device based on a wolf optimization algorithm is characterized by comprising the following components:

at least one processor;

at least one memory for storing at least one program;

when executed by the at least one processor, cause the at least one processor to implement a wolf optimization algorithm-based fault detection method as claimed in any one of claims 1 to 6.

9. A computer readable storage medium in which a processor executable program is stored, wherein the processor executable program, when executed by a processor, is adapted to perform a fault detection method based on a gray wolf optimization algorithm as claimed in any one of claims 1 to 6.