CN110097143A - A kind of Fault Diagnosis of Gear Case method based on fish-swarm algorithm Optimizing BP Network - Google Patents

A kind of Fault Diagnosis of Gear Case method based on fish-swarm algorithm Optimizing BP Network Download PDF

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CN110097143A
CN110097143A CN201910455439.6A CN201910455439A CN110097143A CN 110097143 A CN110097143 A CN 110097143A CN 201910455439 A CN201910455439 A CN 201910455439A CN 110097143 A CN110097143 A CN 110097143A
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唐刚
朱立军
胡雄
周浩
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Shanghai Maritime University
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Abstract

The invention discloses a kind of Fault Diagnosis of Gear Case methods based on fish-swarm algorithm Optimizing BP Network.For the mechanism of ammonium fixation in two the major parameter visuals field and step-length in standard intraocular's fish-swarm algorithm, the present invention first improves original fish-swarm algorithm, propose a kind of dynamic self-adapting artificial fish-swarm algorithm (DLAFSA), recycle the initial weight and threshold value of dynamic self-adapting artificial fish school algorithm BP neural network, DLAFSA-BP network model is constructed, and is applied to Fault Diagnosis of Gear Case.The present invention has given full play to the global optimizing ability of algorithm operation artificial fish-swarm algorithm early period and the local search ability that later period BP neural network is fine, can more acurrate, reliably identify the failure of gear-box.Those skilled in the art only needs for this method to be used for collected gear-box vibration data, can judge fault type, while solving the problems, such as that the precision of conventional gearbox fault diagnosis technology is low, poor reliability.

Description

A kind of Fault Diagnosis of Gear Case method based on fish-swarm algorithm Optimizing BP Network
Technical field
The present invention relates to fault diagnosis technology field, especially a kind of gear-box event based on fish-swarm algorithm Optimizing BP Network Hinder diagnostic method.
Background technique
Gear-box as a kind of actuating unit, be one of with widest mechanical equipment in real life, such as Wind-driven generator, high speed motor car and space equipment etc., gear-box are wherein play extremely critical role.Due in gear-box Structure is complicated in portion, and working environment is severe, and the processing technologys such as components therein such as gear, bearing are complicated, assembly precision requirement Height, and the often continuous work under high-speed overload are the main reason for inducing mechanical disorder, in some instances it may even be possible to will lead to entire machinery The paralysis of system causes the loss that can not be retrieved.Therefore, the operating status of Accurate Diagnosis gear-box whether health and fault bit It sets significant.For the troubleshooting issue of gear-box, currently, domestic and foreign scholars propose the method for many fault diagnosises, From the time domain of early stage, frequency domain analysis Time-frequency Analysis finally and present machine learning field.North China Electric Power University Dragon's fountain etc. benefit is proposed in paper " the gearbox of wind turbine method for diagnosing faults based on particle group optimizing BP neural network " With the weight and threshold value of particle swarm algorithm Optimized BP Neural Network, and by the network after optimization be used for gearbox of wind turbine therefore Barrier diagnosis, this method lack the dynamic regulation of speed, fall into locally optimal solution easily to reduce diagnostic accuracy.Institutes Of Technology Of He'nan Liu Jingyan proposed in paper " application of the genetic neural network in Fault Diagnosis of Gear Case " it is a kind of based on genetic algorithm The Fault Diagnosis of Gear Case method of Optimized BP Neural Network, this method have dependence, and operation for the selection of initial population Amount is big.
These method for diagnosing faults are all inevitably present respective defect, limit fault diagnosis technology in engineering With the application in actual production.It can be seen that the problem of precision of existing Gear Box Fault Diagnosis Technology is low, poor reliability is urgently It solves.
Summary of the invention
For the disadvantages described above of the prior art, higher, good reliability that the main purpose of the present invention is to provide a kind of precision Fault Diagnosis of Gear Case method.
To achieve the above object, the present invention provides a kind of Fault Diagnosis of Gear Case based on fish-swarm algorithm Optimizing BP Network Method, comprising the following steps:
1) using the vibration signal of acceleration transducer acquisition gear-box speed change system, normal, retainer fracture event is obtained Vibration under five kinds of barrier, broken teeth failure, bearing outer ring failure, combined fault (existing simultaneously gear tooth breakage and outer ring crackle) states Data；
2) Fault characteristic parameters are extracted, feature ginseng is extracted according to sensibility of each index to five kinds of gear-box different operating conditions Number, and be normalized；
3) using said extracted and make the characteristic parameter of normalized as dynamic self-adapting artificial fish school algorithm Five kinds of states of the input of BP neural network (DLAFSA-BP), gear-box are exported as network；
4) Fault Diagnosis of Gear Case result is obtained using DLAFSA-BP network model.
The improvement content of standard intraocular's fish-swarm algorithm is specific as follows:
1) mechanism of ammonium fixation in two major parameter visuals field and step-length in standard intraocular's fish-swarm algorithm is adjusted to a kind of dynamic State mechanism is adjusted parameter visual using the dynamic changeability of Logistic model, if the variation model of visual field visual It encloses for [visualmin, visualmax], the variation of parameter visual is set as formula:
The then calculation formula of visual are as follows:
Wherein, t is the number of iterations of algorithm；α is initial attenuation speed, and α value is bigger, and visual decrease speed is faster.
Step-length Step is set with the dynamic self-adapting changing rule such as following formula of visual field visual:
Step=β * Visual (3)
Wherein, β is view step coefficient 0 < β < 1.
2) increase bulletin board in improved fish-swarm algorithm, ignore original crowding factor.It is recorded in bulletin board each Artificial Fish executes the highest Artificial Fish state of food concentration after a behavior, is always ensured that Artificial Fish in bulletin board in iterative process State be optimal.
Shoal of fish behavior representation formula of bunching in conjunction with bulletin board is as follows:
Wherein, rand indicates a random number between [0,1]；XcWhen indicating the t times iteration, n (t) neighbours in neighborhood The center of fish；Ggbx indicates the state of Artificial Fish in bulletin board；Xnext(i) next state of i-th Artificial Fish is indicated.
X in formula (4)cCalculation formula it is as follows:
Wherein, XmiIndicate Artificial Fish XiM (0 < m < n (t)) article neighbours fish.
Shoal of fish foraging behavior expression in conjunction with bulletin board is as follows:
Wherein, XmaxIndicate Artificial Fish XiThat highest neighbours fish of food concentration in neighbour structure.
Using the adaptive dynamic shoal of fish algorithm optimization BP neural network, the specific steps of which are as follows:
1) BP neural network topological structure is determined, the neuron number including the network number of plies and each layer network；
2) according to N Artificial Fish of network structure random initializtion, the initial shoal of fish is formed；
3) parameter of dynamic self-adapting artificial fish-swarm algorithm (DLAFSA) is set, including repeats to explore number try- Number, maximum number of iterations T, field range [visualmin, visualmax], rate of decay α, depending on walking factor beta；
4) food concentration of every Artificial Fish in the initial shoal of fish is calculated.The state of Artificial Fish is set as the initial of neural network Weight and threshold value, every Artificial Fish all correspond to a neural network, food of the inverse of neural metwork training error as Artificial Fish Object concentration；
5) DLAFSA algorithm is run, after algorithm, extracts the state ggbx of Artificial Fish in bulletin board；
6) the state ggbx of Artificial Fish in bulletin board is assigned to BP neural network as initial weight and threshold value；
7) training BP neural network, and fault diagnosis is carried out with trained network.
The state of Artificial Fish is set as to the initial weight and threshold value of neural network, the conduct reciprocal of neural metwork training error The food concentration of Artificial Fish, then network weight and the searching process of threshold value are as follows:
The state X of Artificial Fish is expressed as an I*J+J+J*K+K dimension by the neural network for being I-J-K to a topological structure Vector:
X=(w11...wi1,b1,...,w1j...wij,bj,v11...vj1,a1,...,v1k...vjk,ak) (7)
Wherein, w11...wi1Indicate weight of the input layer to first neuron of hidden layer, b1Indicate hidden layer the The threshold value of one neuron, w1j...wijIndicate the weight between input layer and hidden layer j-th neuron, bjIt indicates The threshold value of hidden layer j-th neuron, v11...vj1Indicate the power between first neuron of hidden layer neuron and output layer Value, a1Indicate the threshold value of first neuron of hidden layer, v1k...vjkIndicate hidden layer neuron and k-th of neuron of output layer Between weight, akIndicate the threshold value of k-th of neuron of hidden layer.BP neural network instruction is set by the food concentration of Artificial Fish Practice the inverse of error, i.e. Y (X)=1/e (X), then any two Artificial Fish XpAnd XqBetween Euclidean distance calculation formula it is as follows:
Wherein Xp、XqIn element subtract each other in strict accordance with dimension one-to-one correspondence, an Artificial Fish basic act of every executions is refreshing It is just adjusted through network weight and threshold value primary.
In general, the gear-box event of the present invention based on dynamic self-adapting artificial fish school algorithm BP neural network Hinder diagnostic method compared with prior art, can achieve the following beneficial effects:
The present invention is proposed for the mechanism of ammonium fixation in two the major parameter visuals field and step-length in the artificial fish-swarm algorithm of standard A kind of dynamic self-adapting artificial fish-swarm algorithm, improves the speed and precision of algorithm global optimizing；It is examined in the failure of gear-box Faulted-stage section utilizes the initial weight and threshold value of dynamic self-adapting artificial fish school algorithm BP neural network, the DLAFSA- of building BP network model can accurately and reliably identify fault type.Those skilled in the art only needs to be used to collect by this method Gear-box vibration data, can judge fault type.
Detailed description of the invention
Invention is described further with reference to the accompanying drawing:
Fig. 1 is that the Fault Diagnosis of Gear Case of the present invention based on dynamic self-adapting fish-swarm algorithm Optimized BP Neural Network is total Body flow chart；
Fig. 2 is dynamic self-adapting artificial fish-swarm algorithm flow chart of the present invention；
Fig. 3 is the flow chart of dynamic self-adapting artificial fish school algorithm BP neural network of the present invention；
Fig. 4 is the gear-box event based on dynamic self-adapting artificial fish school algorithm BP neural network of the embodiment of the present invention Hinder diagnostic result figure.
Specific embodiment
It is clear to be more clear the objectives, technical solutions, and advantages of the present invention, below in conjunction with the drawings and specific embodiments The present invention will be described in further detail.
Fig. 1 is that the gearbox fault of the present invention based on dynamic self-adapting artificial fish school algorithm BP neural network is examined Disconnected overview flow chart.Believe as shown in Figure 1, acquiring the vibration under five kinds of operating conditions of gear-box speed change system with acceleration transducer first Number；Extract input of the Fault characteristic parameters as dynamic self-adapting artificial fish school algorithm BP neural network model；Iteration After the completion of training, output gear case fault diagnosis result.
Fig. 2 is dynamic self-adapting artificial fish-swarm algorithm flow chart of the present invention.As shown in Fig. 2, the shoal of fish is initialized first, The food concentration of every Artificial Fish is calculated, and assigns the highest Artificial Fish of food concentration to bulletin board, then executes fish school behavior Rule updates the value in the state and bulletin board of Artificial Fish, judges whether to meet termination condition, exports bulletin board if meeting Value, carries out next iteration if being unsatisfactory for, until meeting termination condition.
The mechanism of ammonium fixation in two major parameter visuals field and step-length in standard intraocular's fish-swarm algorithm is adjusted to a kind of dynamic Mechanism is adjusted parameter visual using the dynamic changeability of Logistic model, if the variation range of visual field visual For [visualmin, visualmax], the variation of parameter visual is set as formula:
The then calculation formula of visual are as follows:
Wherein, t is the number of iterations of algorithm；α is initial attenuation speed, and α value is bigger, and visual decrease speed is faster.
Step-length Step with visual field visual dynamic self-adapting changing rule such as following formula:
Step=β * Visual (3)
Wherein, β is view step coefficient (0 < β < 1).
Shoal of fish behavior representation formula of bunching in conjunction with bulletin board is as follows:
Wherein, rand indicates a random number between [0,1]；XcWhen indicating the t times iteration, n (t) neighbours in neighborhood The center of fish；Ggbx indicates the state of Artificial Fish in bulletin board；Xnext(i) next state of i-th Artificial Fish is indicated.
X in formula (4)cCalculation formula it is as follows:
Wherein, XmiIndicate Artificial Fish XiM (0 < m < n (t)) article neighbours fish.
Shoal of fish foraging behavior expression in conjunction with bulletin board is as follows:
Wherein, XmaxIndicate Artificial Fish XiThat highest neighbours fish of food concentration in neighbour structure.
Fig. 3 is the flow chart of dynamic self-adapting fish-swarm algorithm Optimized BP Neural Network of the present invention.As shown in figure 3, DLAFSA algorithm optimization BP neural network the following steps are included:
1) BP neural network topological structure is determined, the neuron number including the network number of plies and each layer network；
2) according to N Artificial Fish of network structure random initializtion, the initial shoal of fish is formed；
3) parameter of improved artificial fish-swarm algorithm (DLAFSA) is set, including repeats to explore number try-number, Maximum number of iterations T, field range [visualmin, visualmax], rate of decay α, depending on walking factor beta；
4) food concentration of every Artificial Fish in the initial shoal of fish is calculated.The state of Artificial Fish is set as the initial of neural network Weight and threshold value, every Artificial Fish all correspond to a neural network, food of the inverse of neural metwork training error as Artificial Fish Object concentration；
5) DLAFSA algorithm is run, after algorithm, extracts the Artificial Fish state ggbx in bulletin board；
6) the state ggbx of Artificial Fish in bulletin board is assigned to BP neural network as initial weight and threshold value；
7) training BP neural network, and fault diagnosis is carried out with the network after training.
In step 4), the state of Artificial Fish is set as to the initial weight and threshold value of neural network, neural metwork training error Food concentration of the inverse as Artificial Fish, then network weight and the searching process of threshold value are as follows:
The state X of Artificial Fish is expressed as an I*J+J+J*K+K dimension by the neural network for being I-J-K to a topological structure Vector:
X=(w11...wi1,b1,...,w1j...wij,bj,v11...vj1,a1,...,v1k...vjk,ak) (7)
Wherein, w11...wi1Indicate weight of the input layer to first neuron of hidden layer, b1Indicate hidden layer the The threshold value of one neuron, w1j...wijIndicate the weight between input layer and hidden layer j-th neuron, bjIt indicates The threshold value of hidden layer j-th neuron, v11...vj1Indicate the power between first neuron of hidden layer neuron and output layer Value, a1Indicate the threshold value of first neuron of hidden layer, v1k...vjkIndicate hidden layer neuron and k-th of neuron of output layer Between weight, akIndicate the threshold value of k-th of neuron of hidden layer.BP neural network instruction is set by the food concentration of Artificial Fish Practice the inverse of error, i.e. Y (X)=1/e (X), then any two Artificial Fish XpAnd XqBetween Euclidean distance calculation formula it is as follows:
Wherein Xp、XqIn element subtract each other in strict accordance with dimension one-to-one correspondence, an Artificial Fish basic act of every executions is refreshing It is just adjusted through network weight and threshold value primary.
Using acceleration transducer acquisition gear-box in normal, retainer fracture defect, broken teeth failure, bearing outer ring event Vibration under five kinds of barrier, retainer fracture defect and combined fault (existing simultaneously gear tooth breakage failure and outer ring crack fault) operating conditions Dynamic 50 groups of signal, every kind 10 groups of operating condition, wherein 8 groups are used as training sample, 2 groups are used as test sample, extract nargin, kurtosis, partially Eight coefficient, pulse index, barycenter of frequency spectrum, Spectral variance, harmonic factor and correlation factor characteristic parameters are spent as DLAFSA-BP The input of network model, output use binary coding representation are as follows: normal (1 000 0), retainer fracture (0 100 0), broken teeth (0 010 0), bearing outer ring crackle (0 001 0), combined fault (0 000 1).
The parameter setting of ADAFSA algorithm: artificial fish-swarm scale N is 50, regard step factor beta as 0.6, and rate of decay α is 100, Field range [1,8], maximum number of iterations T are 100, repeat to explore number try-number to be 30.
The parameter setting of BP neural network: network topology structure 8-8-5.Hidden layer is adopted respectively with output layer transmission function With tansig function and logsig function, train function sets for trainlm, frequency of training 1000, training objective 0.0001, Learning rate 0.05.
Fig. 4 is that the gearbox fault based on dynamic self-adapting artificial fish school algorithm BP neural network is examined in the present embodiment Disconnected result figure.Can be seen that from Fig. 4 test result can using the BP neural network model after the optimization of dynamic self-adapting fish-swarm algorithm Accurately to identify gearbox fault type, high reliablity.
In conclusion the above is merely preferred embodiments of the present invention, the protection scope being not intended to limit the invention. All within the spirits and principles of the present invention, made any modification, equivalent improvement, replacement etc., should all include of the invention Within protection scope.

Claims (2)

1. a kind of Fault Diagnosis of Gear Case method based on fish-swarm algorithm Optimizing BP Network, which comprises the following steps:
Step 1: obtaining normal, retainer fracture event using the vibration signal of acceleration transducer acquisition gear-box speed change system Vibration under five kinds of barrier, broken teeth failure, bearing outer ring failure, combined fault (existing simultaneously gear tooth breakage and outer ring crackle) states Data；
Step 2: extracting Fault characteristic parameters, feature ginseng is extracted according to sensibility of each index to five kinds of gear-box different operating conditions Number, and be normalized；
Step 3: using said extracted and make normalized characteristic parameter as dynamic self-adapting artificial fish school algorithm Five kinds of states of the input of BP neural network (DLAFSA-BP), gear-box are exported as network；
Step 4: obtaining Fault Diagnosis of Gear Case result using DLAFSA-BP network model.
2. the Fault Diagnosis of Gear Case method according to claim 1 based on fish-swarm algorithm Optimizing BP Network, feature exist In in step 3, the mechanism of ammonium fixation in two major parameter visuals field and step-length in standard intraocular's fish-swarm algorithm is adjusted to a kind of Dynamic mechanism is adjusted parameter visual using the dynamic changeability of Logistic model, if the variation of visual field visual Range is [visualmin, visualmax], the variation of parameter visual is set as formula:
The then calculation formula of visual are as follows:
Wherein, t is the number of iterations of algorithm；α is initial attenuation speed, and α value is bigger, and visual decrease speed is faster；
Step-length Step is set with the dynamic self-adapting changing rule such as following formula of visual field visual:
Step=β * Visual (3)
Wherein, β is view step coefficient 0 < β < 1；
2) in improved fish-swarm algorithm, ignore original crowding factor.Each Artificial Fish is recorded in bulletin board executes one The highest Artificial Fish state of food concentration after behavior is always ensured that the state of Artificial Fish in bulletin board is optimal in iterative process 's；
Shoal of fish Behavior changes of bunching in conjunction with bulletin board are as follows:
Wherein, rand indicates a random number between [0,1]；XcWhen indicating the t times iteration, n (t) neighbours fish in neighborhood Center；Ggbx indicates the state of Artificial Fish in bulletin board；Xnext(i) next state of i-th Artificial Fish is indicated；
X in formula (4)cCalculation formula it is as follows:
Wherein, XmiIndicate Artificial Fish XiM (0 < m < n (t)) article neighbours fish；
Shoal of fish foraging behavior expression in conjunction with bulletin board is as follows:
Wherein, XmaxIndicate Artificial Fish XiThat highest neighbours fish of food concentration in neighbour structure；
Using the adaptive dynamic shoal of fish algorithm optimization BP neural network, the specific steps of which are as follows:
1) BP neural network topological structure is determined, the neuron number including the network number of plies and each layer network；
2) according to N Artificial Fish of network structure random initializtion, the initial shoal of fish is formed；
3) parameter of dynamic self-adapting artificial fish-swarm algorithm (DLAFSA) is set, including repeats to explore number try-number, most Big the number of iterations T, field range [visualmin, visualmax], rate of decay α, depending on walking factor beta；
4) food concentration of every Artificial Fish in the initial shoal of fish is calculated.The state of Artificial Fish is set as to the initial weight of neural network And threshold value, every Artificial Fish all correspond to a neural network, the inverse of neural metwork training error is dense as the food of Artificial Fish Degree；
5) DLAFSA algorithm is run, after algorithm, extracts the state ggbx of Artificial Fish in bulletin board；
6) the state ggbx of Artificial Fish in bulletin board is assigned to BP neural network as initial weight and threshold value；
7) training BP neural network, and fault diagnosis is carried out with trained network；
The state of Artificial Fish is set as to the initial weight and threshold value of neural network, the reciprocal of neural metwork training error is used as manually The food concentration of fish, then network weight and the searching process of threshold value are as follows:
The neural network for being I-J-K to a topological structure, is expressed as an I*J+J+J*K+K dimensional vector for the state X of Artificial Fish:
X=(w11...wi1,b1,...,w1j...wij,bj,v11...vj1,a1,...,v1k...vjk,ak) (7)
Wherein, w11...wi1Indicate weight of the input layer to first neuron of hidden layer, b1Indicate hidden layer first The threshold value of neuron, w1j...wijIndicate the weight between input layer and hidden layer j-th neuron, bjIndicate implicit The threshold value of layer j-th neuron, v11...vj1Indicate the weight between first neuron of hidden layer neuron and output layer, a1 Indicate the threshold value of first neuron of hidden layer, v1k...vjkIt indicates between k-th of neuron of hidden layer neuron and output layer Weight, akIndicate the threshold value of k-th of neuron of hidden layer.BP neural network training is set by the food concentration of Artificial Fish to miss The inverse of difference, i.e. Y (X)=1/e (X), then any two Artificial Fish XpAnd XqBetween Euclidean distance calculation formula it is as follows:
Wherein Xp、XqIn element in strict accordance with dimension one-to-one correspondence subtract each other, an Artificial Fish basic act of every execution, nerve net Network weight and threshold value just adjust once.
CN201910455439.6A 2019-05-29 2019-05-29 A kind of Fault Diagnosis of Gear Case method based on fish-swarm algorithm Optimizing BP Network Withdrawn CN110097143A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108459406A (en) * 2018-03-15 2018-08-28 上海理工大学 Microscope auto-focusing window selection method based on artificial fish-swarm algorithm
CN112446457A (en) * 2020-12-02 2021-03-05 中国计量大学 Gas leakage source positioning method based on improved artificial fish swarm algorithm

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108459406A (en) * 2018-03-15 2018-08-28 上海理工大学 Microscope auto-focusing window selection method based on artificial fish-swarm algorithm
CN108459406B (en) * 2018-03-15 2020-06-23 上海理工大学 Microscope automatic focusing window selection method based on artificial fish swarm algorithm
CN112446457A (en) * 2020-12-02 2021-03-05 中国计量大学 Gas leakage source positioning method based on improved artificial fish swarm algorithm
CN112446457B (en) * 2020-12-02 2023-07-18 中国计量大学 Gas leakage source positioning method based on improved artificial fish swarm algorithm

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Application publication date: 20190806