CN116894376A - Ship diesel generator fault diagnosis method based on integrated deep learning - Google Patents
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Abstract
The invention discloses a fault diagnosis method of a ship diesel generator based on integrated deep learning, which comprises the following steps: (1) Collecting original fault data of a multichannel one-dimensional time sequence of a ship diesel generator; (2) Constructing a multichannel one-dimensional time sequence data fusion data set; (3) Performing data truncation processing based on a sliding window overlapping sampling method to generate a standard two-dimensional feature map training sample total data set; (4) dividing into a training set, a verification set and a test set; (5) Constructing an integrated deep learning space-time feature extraction fault diagnosis model; (6) initializing parameter weights of the diagnostic model; (7) Training and verifying an integrated deep learning space-time feature extraction fault diagnosis model; (8) And testing the trained integrated deep learning space-time feature extraction fault diagnosis model, and finally automatically outputting a diagnosis result by a Softmax layer, so that the fault diagnosis of the ship diesel generator by people is more intelligent and convenient.
Description
Technical Field
The invention belongs to the technical field of fault diagnosis, and particularly relates to a fault diagnosis method of a ship diesel generator based on integrated deep learning.
Background
The marine diesel generator is the most important power source motor in the electric propulsion marine, and continuously operates under complex and changeable sea conditions, and is subjected to severe working environment influences of high temperature, high pressure, damp heat, salt and alkali and variable load so as to easily generate various faults.
Before the invention, the fault diagnosis and state monitoring method for the ship diesel generator is mainly based on the traditional modes of post maintenance, planned maintenance and timing maintenance, the method is quite low in efficiency and does not have intelligence, the parts are periodically maintained and replaced according to experience in the past, the waste and fault misjudgment are easily caused by the rough maintenance mode of empirically estimating the residual life of the parts, the potential safety hazard is brought, and the tiny fault characteristics of the ship diesel generator are difficult to identify.
With the continuous rising of machine learning research, an artificial intelligence-based fault diagnosis method gradually becomes a hotspot for research in the field of fault diagnosis, and main algorithms are as follows: BP neural network (BPNN), support Vector Machine (SVM) and K nearest neighbor method (KNN); through application verification for many years, the three algorithms have poor feature extraction capability due to a shallow network structure, are difficult to adapt to application in the current big data sample environment, and currently, an industry expert usually adopts a method of combining manual feature extraction and a shallow machine learning method to carry out intelligent fault diagnosis; although the above-mentioned existing intelligent fault diagnosis method has been applied to a certain extent and achieves a certain effect, three major disadvantages are still highlighted: (1) Various advanced signal processing technologies must be mastered for feature extraction, feature selection must be accomplished by experience and expertise of engineers, and great subjectivity and blindness exist; (2) The feature extraction is mainly used for solving the problem of specific faults, has poor universality and is difficult to finish in a mass data sample environment; (3) Manually extracted fault characteristics are incomplete, and the characteristics reflecting the micro faults are easy to delete by mistake and are covered by noise; the main reason for the defects is that the network model used in the existing intelligent fault diagnosis method is mostly of a shallow structure, the feature extraction capability is weak, and the fault features are difficult to effectively identify.
In recent years, the deep learning technology as an emerging technology in the artificial intelligence field has the potential of identifying micro fault characteristics due to the strong characteristic extraction capability, especially, a convolutional neural network (Convolumional Neural Nemwork, CNN) is focused by a fault diagnosis field scholars, but the existing method still has some defects, firstly, the existing method still needs to perform characteristic extraction pretreatment on original fault data by using a traditional characteristic extraction method, the strong characteristic extraction capability of the CNN algorithm is not fully utilized, and further improvement of fault diagnosis effects is limited; the second is that the traditional convolutional neural network comprises a network structure part of a full-connection layer with 2-3 layers, which is usually positioned between the last pooling layer and the Softmax classified output layer, because the training parameter quantity generated by the existence of the full-connection layer occupies 80% -90% of the total parameter quantity of CNN, the defect greatly counteracts the advantages of reducing the parameter quantity by pooling dimension reduction of CNN, the structure of the full-connection layer occupies too much calculation resources, meanwhile, the training of CNN models is easy to be caused to be over-fitted, and especially the number of the CNN models increases exponentially along with the increase of the number of the full-connection layers, so that the traditional CNN model is excessively long in test time consumption when being used for fault on-line diagnosis, and the intelligent rapid diagnosis of the faults of the ship diesel generators is not facilitated.
Disclosure of Invention
Aiming at the defects or the needs to be improved in the prior art, the invention provides a fault diagnosis method of a ship diesel generator based on integrated deep learning, which can carry out intelligent fault diagnosis on data collected by the ship diesel generator under various monitoring signals and a plurality of sensors, does not need any manual feature extraction operation in the whole diagnosis process, and overcomes the defect that the existing fault diagnosis method excessively depends on expert priori knowledge; the invention can automatically carry out intelligent fault diagnosis on the ship diesel generator, the whole diagnosis process is automatically completed without manual intervention, and the invention has better operability and lower use threshold, so that the fault diagnosis of the ship diesel generator by people is more intelligent, convenient and quick.
In order to achieve the above purpose, the invention provides a fault diagnosis method for a marine diesel generator based on integrated deep learning, which comprises the following steps:
(1) Collecting original fault data of a multichannel one-dimensional time sequence of a ship diesel generator;
arranging sensors on the ship diesel generator, acquiring one-dimensional time sequence original fault data generated when the ship diesel generator operates in various health states by using the sensors, wherein the number of the sensors is M (namely M channels), the data acquired by each sensor is a continuous one-dimensional time sequence original long data segment, and the sample length of the one-dimensional time sequence original long data segment is L, namely the one-dimensional time sequence original long data segment comprises L data points; the health states of the ship diesel generator are set to be N health states, wherein the health states comprise a normal state and N-1 fault states, and each health state comprises one-dimensional time sequence original fault long data segments of M channels;
(2) Data fusion is carried out on the original fault data of the multichannel one-dimensional time sequence, and a multichannel one-dimensional time sequence data fusion data set { D } Melting and melting ；
Constructing a multichannel one-dimensional time sequence data fusion data set { D }, which is used for training and testing a deep learning diagnosis model, by using the N health state monitoring data Melting and melting The multi-channel one-dimensional time series data fusion dataset { D } Melting and melting Is configured to include N subsets: { D } Melting and melting ={D 1 ,D 2 , ...,D i , ...,D N } Melting and melting Corresponding to N health states, each subset { D } i } Melting and melting Each of which comprises M one-dimensional time series original long data segments with length L, each subset { D i } Melting and melting Namely, the shape after data fusion is [ M, L ]]The method for data fusion is set as follows: a 1 st sensor channel is a one-dimensional time series original long data segment with the length L, a 2 nd sensor channel is a one-dimensional time series original long data segment with the length L, …, and an M sensor channel is a one-dimensional time series original long data segment with the length L; multi-channel one-dimensional time sequence data fusion data set { D }, obtained under N health states Melting and melting The multi-dimensional tensor matrix shape is [ N, M, L]The method comprises the following steps: one-dimensional time series for each of the M sensor channels in N health states The length of the original data segments is L data points;
(3) Data truncation processing is carried out based on a sliding window overlapping sampling method, and a standard two-dimensional feature map training sample total data set { T }, is generated 2D ；
Fusing the acquired multichannel one-dimensional time series data into a data set { D } Melting and melting The shape of each health state is [ M, L ]]The multi-channel one-dimensional time sequence long data segment adopts a sliding window overlapping sampling method to carry out data truncation processing, and the sliding window overlapping sampling method is set to adopt a fixed sliding windowW num The shape after each data fusion is [ M, L ]]Is a two-dimensional matrix feature map { D ] i } Melting and melting With fixed sliding step lengthS num Performing sliding sampling along the time axis direction, wherein the length of the sliding window is as followsW num Width is M, the sliding windowW num Generating and storing all data points in a sliding window as a standard two-dimensional characteristic diagram training sample { every time one step length is movedtA long multi-channel one-dimensional time series data fusion data set { D }, with a length L Melting and melting GeneratingT num Length of isW num Training a sample total dataset { T }, for a short standard two-dimensional feature map 2D Standard two-dimensional feature map training sample total dataset { T } 2D The shape of (C) is [ N, M,T num ,W num ]；
the data truncation processing method based on the sliding window overlap sampling method is generated T num Standard two-dimensional feature map training sample total data set { T } of short time sequence 2D In (a) and (b)T num The calculation method of (1) is as follows:T num =CEILING[（I num -W num +1）/S num ,1]wherein, the method comprises the steps of, wherein,I num representing the number of data points of the input sample,W num representing the length of the sliding window, i.e. the length of the generated standard two-dimensional feature map training samples,S num representing the step size of the sliding window,T num representing the number of new fault samples, CEILING is a rounding function, i.e. a meter in bracketsThe decimal point of the calculation result is set to be rounded to 1;
the data truncation processing method based on the sliding window overlap sampling method is based on the sliding window step lengthS num Is provided with three standard two-dimensional characteristic diagram training sample generation modes, when the sliding window step length is adoptedS num Equal to the length of the sliding windowW num Non-overlapping aliquoting for aliquoting and intercepting when sliding window step lengthS num Length greater than the sliding windowW num At intervals of sparse interception, when the sliding window step lengthS num Less than the length of the sliding windowW num Incremental overlapping sampling for sample expansion;
(4) Dividing a data set;
training a standard two-dimensional feature map into a sample total dataset { T } 2D Each of the health status typesT num The method for dividing the samples into a training set, a verification set and a test set comprises the following steps: first training a standard two-dimensional feature map into a sample total data set { T } 2D Each of the health status typesT num Randomly selecting 30% of the number of samples from the fault samples as a test set, randomly taking out 60% of the remaining 70% of samples to be classified as a training set and 40% of the samples as a verification set; finally, training the standard two-dimensional feature map to obtain a total data set { T }, and 2D all training set samples in N health states in (1) form a total training set { T } Training device Training a standard two-dimensional feature map into a sample total dataset { T } 2D All validation set samples in the N classes of health states constitute a total validation set { T } Verification Training a standard two-dimensional feature map into a sample total dataset { T } 2D All test set samples of N types of health status constitute a total test set { T } Measuring ；
(5) Constructing an integrated deep learning space-time feature extraction fault diagnosis model;
the integrated deep learning space-time feature extraction fault diagnosis model comprises an input data fusion layer, an integrated deep learning space-time feature extraction layer and a Softmax discrimination output layer; the integrated deep learning space-time feature extraction layer is arranged to comprise a long-short-time memory cyclic neural network Layer (LSTM) of a P layer, a one-dimensional convolutional neural network layer (1D-CNN) of a Q layer and a one-dimensional global average pooling layer (1D-GAP) layer, wherein the LSTM layer is used for extracting time-associated memory features from training samples, the one-dimensional convolutional neural network layer is used for extracting depth representative features from received data, and the 1D-GAP layer is used for carrying out data compression and parameter quantity reduction on the received data;
The one-dimensional convolutional neural network layer consists of a one-dimensional convolutional layer, a Relu activation layer, a Dropout layer and a one-dimensional convolutional layer;
the number of layers P of the long-short-time memory circulating neural network layer in the integrated deep learning space-time feature extraction layer is set to be 2-5; the number of layers of the one-dimensional convolutional neural network layer is set to be 2-5;
the input data fusion layer, the integrated deep learning space-time feature extraction layer and the Softmax discrimination output layer are sequentially connected in series, and firstly, the input data fusion layer is used for carrying out data fusion on the acquired multi-channel one-dimensional time sequence original monitoring data and generating a standard two-dimensional feature map training sample total data set { T } 2D Total training set { T } to be generated after data division Training device Total validation set { T } Verification Sum total test set { T } Measuring Outputting the standard two-dimensional feature map training samples of the (1) to an integrated deep learning space-time feature extraction layer; secondly, training a sample total data set { T } of the standard two-dimensional feature map by integrating a plurality of LSTM networks and 1D-CNN networks in the deep learning space-time feature extraction layer 2D Performing feature mining on each sample in the 1D-CNN layer, extracting fault features of N health states of the ship diesel generator, and performing feature compression and parameter quantity reduction on high-dimensional feature data output by the 1D-CNN layer by adopting a one-dimensional global average pooling layer, so that the training speed of the model is further improved, and the waiting time of diagnostic test is reduced; finally, the low-dimensional characteristic sequence data output by the one-dimensional global averaging layer is output to the Softmax discrimination output layer for normalization processing, and the Softmax discrimination output layer outputs a final accuracy result;
(6) Initializing integrated deep learning space-time characteristics to extract parameter weights of a fault diagnosis model;
(7) By usingTotal training set { T } Training device Training the integrated deep learning space-time feature extraction fault diagnosis model by using the standard two-dimensional feature map training samples; performing super-parameter adjustment on the integrated deep learning space-time feature extraction fault diagnosis model to obtain optimal model super-parameters of an integrated deep learning space-time feature extraction layer in the integrated deep learning space-time feature extraction fault diagnosis model; the training process of the integrated deep learning space-time feature extraction fault diagnosis model is set to train and learn model parameters of each of the integrated deep learning space-time feature extraction layer and the deep feature extraction layer by repeatedly executing forward propagation and backward propagation iterative computation processes, and the total verification set { T } is called in real time in the training process Verification Cross-verifying the diagnosis model in the training process by the verification sample in the process to judge whether the training of the integrated deep learning space-time feature extraction fault diagnosis model is fitted or not;
the method for judging whether the training process of the integrated deep learning space-time feature extraction fault diagnosis model is over-fitted or not comprises the following steps: when the total training set { T } Training device Sum total validation set { T } Verification The accuracy of the samples of (2) increases with the number of training rounds and the total training set { T }, is increased Training device Accuracy of training samples of (1) and total validation set { T } Verification When the accuracy rates of the verification samples are close to and consistent with each other, the model parameter training is normal, and the step (7) is continuously executed for training; when the total training set { T } Training device Sum total validation set { T } Verification The accuracy of the samples of (1) increases with the number of training rounds, but the total validation set { T } Verification The accuracy of the validation samples of (1) starts to be lower than the total training set { T } Training device When the accuracy of the training samples of the integrated deep learning space-time feature extraction fault diagnosis model is not fitted and the accuracy of the verification set reaches a set target value or iteration number, the model training is finished, and the integrated deep learning space-time is saved at the same timeThe method comprises the steps of extracting optimal parameter weight values of each network layer when the accuracy of a verification set in a fault diagnosis model training process is highest;
(8) Using the total test set { T } Measuring The standard two-dimensional feature map test sample or the new fault sample tests the trained integrated deep learning space-time feature extraction fault diagnosis model, and directly outputs a fault diagnosis result.
The invention designs that the sensor is configured as a vibration acceleration sensor.
The invention designs that the sensor also comprises one or a plurality of combinations of common fault monitoring sensors such as a noise sensor, a pressure sensor, a displacement sensor, a rotating speed sensor, a voltage sensor, a current sensor and the like.
The invention designs that the different types and numbers of sensors for data acquisition of the marine diesel generator are arranged to have the same sampling frequency.
The invention has the beneficial effects that: the invention designs an integrated deep learning space-time feature extraction layer by improving the model structure of a traditional one-dimensional convolutional neural network, wherein the integrated deep learning space-time feature extraction layer is arranged to comprise a multi-layer long-short-time memory cyclic neural network Layer (LSTM), a multi-layer one-dimensional convolutional neural network layer (1D-CNN) and a one-dimensional global average pooling layer (1D-GAP), the LSTM layer is used for extracting time-associated memory features from training samples, the one-dimensional convolutional neural network layer is used for extracting depth representative features from received data, and the 1D-GAP layer is used for carrying out data compression and parameter quantity reduction on the received data, so that the model training parameter quantity of the 1D-CNN is effectively reduced, the training speed of the model is improved and the diagnosis test time is reduced; therefore, the invention can carry out intelligent, efficient and rapid data fusion, time-related characteristic information mining, characteristic extraction and fault automatic identification and diagnosis on the multi-sensor one-dimensional time sequence data of the ship diesel generator. The invention does not need any manual feature extraction operation, does not need operators to master complex multiple advanced signal processing technologies, and the whole diagnosis process is automatically completed without manual intervention, so that the invention has better universality and operability, and enables technicians to diagnose the faults of the ship diesel generator more intelligently, conveniently and rapidly.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
fig. 1 is a method step diagram of an embodiment of the present invention.
FIG. 2 is a diagram of multi-sensor multi-channel one-dimensional time series data fusion according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a method for generating a standard two-dimensional feature map training sample by data truncation processing based on a sliding window overlap sampling method according to an embodiment of the present invention.
FIG. 4 is a schematic diagram of a structural framework of an integrated deep learning spatiotemporal feature extraction fault diagnosis model according to an embodiment of the present invention.
FIG. 5 is a flow chart of the algorithm logic of the integrated deep learning spatiotemporal feature extraction fault diagnosis model of an embodiment of the present invention.
Fig. 6 is a schematic diagram of a test stand for collecting time-varying rotational speed failure data of a rolling bearing according to a preferred embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1 to 6, the fault diagnosis method for a marine diesel generator based on integrated deep learning according to the preferred embodiment of the present invention, as shown in fig. 1, includes the following steps:
S101, collecting multichannel one-dimensional time sequence original fault data of a ship diesel generator;
arranging sensors on the ship diesel generator, acquiring one-dimensional time sequence original fault data generated when the ship diesel generator operates in various health states by using the sensors, wherein the number of the sensors is M (namely M channels), the data acquired by each sensor is a continuous one-dimensional time sequence original long data segment, and the sample length of the one-dimensional time sequence original long data segment is L, namely the one-dimensional time sequence original long data segment comprises L data points; the health states of the ship diesel generator are set to be N health states, wherein the health states comprise a normal state and N-1 fault states, and each health state comprises one-dimensional time sequence original fault long data segments of M channels;
s102, carrying out data fusion on the original fault data of the multichannel one-dimensional time sequence, and constructing a multichannel one-dimensional time sequence data fusion data set { D } Melting and melting ；
Constructing a multichannel one-dimensional time sequence data fusion data set { D }, which is used for training and testing a deep learning diagnosis model, by using the N health state monitoring data Melting and melting The multi-channel one-dimensional time series data fusion dataset { D } Melting and melting Is configured to include N subsets: { D } Melting and melting ={D 1 ,D 2 , ...,D i , ...,D N } Melting and melting Corresponding to N health states, each subset { D } i } Melting and melting Each of which comprises M one-dimensional time series original long data segments with length L, each subset { D i } Melting and melting Namely, the shape after data fusion is [ M, L ]]The method for data fusion is set as follows: a 1 st sensor channel is a one-dimensional time series original long data segment with the length L, a 2 nd sensor channel is a one-dimensional time series original long data segment with the length L, …, and an M sensor channel is a one-dimensional time series original long data segment with the length L; multi-channel one-dimensional time sequence data fusion data set { D }, obtained under N health states Melting and melting The multi-dimensional tensor matrix shape is [ N, M, L]The method comprises the following steps: the length of each one-dimensional time sequence original data segment of M sensor channels under N health states is L data points;
s103, carrying out data truncation processing based on a sliding window overlap sampling method to generate a standard two-dimensional feature map training sample total data set { T } 2D ；
The acquired multichannel one-dimensional time sequence dataFusion dataset { D } Melting and melting The shape of each health state is [ M, L ] ]The multi-channel one-dimensional time sequence long data segment adopts a sliding window overlapping sampling method to carry out data truncation processing, and the sliding window overlapping sampling method is set to adopt a fixed sliding windowW num The shape after each data fusion is [ M, L ]]Is a two-dimensional matrix feature map { D ] i } Melting and melting With fixed sliding step lengthS num Performing sliding sampling along the time axis direction, wherein the length of the sliding window is as followsW num Width is M, the sliding windowW num Generating and storing all data points in a sliding window as a standard two-dimensional characteristic diagram training sample { every time one step length is movedtA long multi-channel one-dimensional time series data fusion data set { D }, with a length L Melting and melting GeneratingT num Length of isW num Training a sample total dataset { T }, for a short standard two-dimensional feature map 2D Standard two-dimensional feature map training sample total dataset { T } 2D The shape of (C) is [ N, M,T num ,W num ]；
the data truncation processing method based on the sliding window overlap sampling method is generatedT num Standard two-dimensional feature map training sample total data set { T } of short time sequence 2D In (a) and (b)T num The calculation method of (1) is as follows:T num =CEILING[（I num -W num +1）/S num ,1]wherein, the method comprises the steps of, wherein,I num representing the number of data points of the input sample,W num representing the length of the sliding window, i.e. the length of the generated standard two-dimensional feature map training samples, S num Representing the step size of the sliding window,T num representing the number of new fault samples, wherein CEILING is a rounding function, namely the decimal point of the calculation result in the middle brackets is set to be rounded to 1; in FIG. 3, it is assumed that each channel contains 2000 data points, i.eI num =2000, by the overlap sampling data truncation method, 6 training samples (sliding window lengthW num 400 stepsS num 300, i.e. an overlap of 25%)Each failure sample has a size of [ m, 400]. As can be seen from fig. 3, the rightmost sample is incomplete, which cannot constitute a valid sample, which is discarded.
The data truncation processing method based on the sliding window overlap sampling method is based on the sliding window step lengthS num Is provided with three standard two-dimensional characteristic diagram training sample generation modes, when the sliding window step length is adoptedS num Equal to the length of the sliding windowW num Non-overlapping aliquoting for aliquoting and intercepting when sliding window step lengthS num Length greater than the sliding windowW num At intervals of sparse interception, when the sliding window step lengthS num Less than the length of the sliding windowW num Incremental overlapping sampling for sample expansion;
s104, dividing a data set;
training a standard two-dimensional feature map into a sample total dataset { T } 2D Each of the health status types T num The method for dividing the samples into a training set, a verification set and a test set comprises the following steps: first training a standard two-dimensional feature map into a sample total data set { T } 2D Each of the health status typesT num Randomly selecting 30% of the number of samples from the fault samples as a test set, randomly taking out 60% of the remaining 70% of samples to be classified as a training set and 40% of the samples as a verification set; finally, training the standard two-dimensional feature map to obtain a total data set { T }, and 2D all training set samples in N health states in (1) form a total training set { T } Training device Training a standard two-dimensional feature map into a sample total dataset { T } 2D All validation set samples in the N classes of health states constitute a total validation set { T } Verification Training a standard two-dimensional feature map into a sample total dataset { T } 2D All test set samples of N types of health status constitute a total test set { T } Measuring ；
S105, constructing an integrated deep learning space-time feature extraction fault diagnosis model;
the integrated deep learning space-time feature extraction fault diagnosis model comprises an input data fusion layer, an integrated deep learning space-time feature extraction layer and a Softmax discrimination output layer; the integrated deep learning space-time feature extraction layer is arranged to comprise a long-short-time memory cyclic neural network Layer (LSTM) of a P layer, a one-dimensional convolutional neural network layer (1D-CNN) of a Q layer and a one-dimensional global average pooling layer (1D-GAP) layer, wherein the LSTM layer is used for extracting time-associated memory features from training samples, the one-dimensional convolutional neural network layer is used for extracting depth representative features from received data, and the 1D-GAP layer is used for carrying out data compression and parameter quantity reduction on the received data;
The one-dimensional convolutional neural network layer consists of a one-dimensional convolutional layer, a Relu activation layer, a Dropout layer and a one-dimensional convolutional layer;
the number of layers P of the long-short-time memory circulating neural network layer in the integrated deep learning space-time feature extraction layer is set to be 2-5; the number of layers of the one-dimensional convolutional neural network layer is set to be 2-5;
the input data fusion layer, the integrated deep learning space-time feature extraction layer and the Softmax discrimination output layer are sequentially connected in series, and firstly, the input data fusion layer is used for carrying out data fusion on the acquired multi-channel one-dimensional time sequence original monitoring data and generating a standard two-dimensional feature map training sample total data set { T } 2D Total training set { T } to be generated after data division Training device Total validation set { T } Verification Sum total test set { T } Measuring Outputting the standard two-dimensional feature map training samples of the (1) to an integrated deep learning space-time feature extraction layer; secondly, training a sample total data set { T } of the standard two-dimensional feature map by integrating a plurality of LSTM networks and 1D-CNN networks in the deep learning space-time feature extraction layer 2D Performing feature mining on each sample in the 1D-CNN layer, extracting fault features of N health states of the ship diesel generator, and performing feature compression and parameter quantity reduction on high-dimensional feature data output by the 1D-CNN layer by adopting a one-dimensional global average pooling layer, so that the training speed of the model is further improved, and the waiting time of diagnostic test is reduced; finally, the low-dimensional characteristic sequence data output by the one-dimensional global averaging layer is output to the Softmax discrimination output layer for normalization processing, and the Softmax discrimination output layer outputs a final accuracy result;
S106, initializing integrated deep learning space-time characteristics to extract parameter weights of a fault diagnosis model;
s107, adopting the total training set { T } Training device Training the integrated deep learning space-time feature extraction fault diagnosis model by using the standard two-dimensional feature map training samples; performing super-parameter adjustment on the integrated deep learning space-time feature extraction fault diagnosis model to obtain optimal model super-parameters of an integrated deep learning space-time feature extraction layer in the integrated deep learning space-time feature extraction fault diagnosis model; the training process of the integrated deep learning space-time feature extraction fault diagnosis model is set to train and learn model parameters of each of the integrated deep learning space-time feature extraction layer and the deep feature extraction layer by repeatedly executing forward propagation and backward propagation iterative computation processes, and the total verification set { T } is called in real time in the training process Verification Cross-verifying the diagnosis model in the training process by the verification sample in the process to judge whether the training of the integrated deep learning space-time feature extraction fault diagnosis model is fitted or not;
the method for judging whether the training process of the integrated deep learning space-time feature extraction fault diagnosis model is over-fitted or not comprises the following steps: when the total training set { T } Training device Sum total validation set { T } Verification The accuracy of the samples of (2) increases with the number of training rounds and the total training set { T }, is increased Training device Accuracy of training samples of (1) and total validation set { T } Verification When the accuracy rates of the verification samples are close to and consistent with each other, the model parameter training is normal, and the step S107 is continuously executed for training; when the total training set { T } Training device Sum total validation set { T } Verification The accuracy of the samples of (1) increases with the number of training rounds, but the total validation set { T } Verification The accuracy of the validation samples of (1) starts to be lower than the total training set { T } Training device When the accuracy of the training samples of the integrated deep learning space-time feature extraction fault diagnosis model is higher than the accuracy of the training samples, the parameter training of the diagnosis model is fitted, the model training is stopped immediately, the step S105 is skipped, the super-parameters of the integrated deep learning space-time feature extraction fault diagnosis model are modified again, and the process is repeatedly performed until the integrated deep learning space-time feature extraction fault diagnosis model is not fitted and the verification set is verifiedWhen the accuracy reaches a set target value or iteration round number, model training is finished, and meanwhile, the optimal parameter weight value of each network layer when the accuracy of a verification set in the integrated deep learning space-time feature extraction fault diagnosis model training process is highest is saved;
S108, adopting a total test set { T } Measuring The standard two-dimensional feature map test sample or the new fault sample tests the trained integrated deep learning space-time feature extraction fault diagnosis model, and directly outputs a fault diagnosis result.
In this embodiment, the sensor is provided as a vibration acceleration sensor.
In this embodiment, the sensor further includes one or more combinations of common fault monitoring sensors such as a noise sensor, a pressure sensor, a displacement sensor, a rotation speed sensor, a voltage sensor, and a current sensor.
In this embodiment, the different types and numbers of sensors for data acquisition of the marine diesel generator are set to have the same sampling frequency.
The invention has the beneficial effects that: the invention designs an integrated deep learning space-time feature extraction layer by improving the model structure of a traditional one-dimensional convolutional neural network, wherein the integrated deep learning space-time feature extraction layer is arranged to comprise a multi-layer long-short-time memory cyclic neural network Layer (LSTM), a multi-layer one-dimensional convolutional neural network layer (1D-CNN) and a one-dimensional global average pooling layer (1D-GAP), the LSTM layer is used for extracting time-associated memory features from training samples, the one-dimensional convolutional neural network layer is used for extracting depth representative features from received data, and the 1D-GAP layer is used for carrying out data compression and parameter quantity reduction on the received data, so that the model training parameter quantity of the 1D-CNN is effectively reduced, the training speed of the model is improved and the diagnosis test time is reduced; therefore, the invention can carry out intelligent, efficient and rapid data fusion, time-related characteristic information mining, characteristic extraction and fault automatic identification and diagnosis on the multi-sensor one-dimensional time sequence data of the ship diesel generator. The invention does not need any manual feature extraction operation, does not need operators to master complex multiple advanced signal processing technologies, and the whole diagnosis process is automatically completed without manual intervention, so that the invention has better universality and operability, and enables technicians to diagnose the faults of the ship diesel generator more intelligently, conveniently and rapidly.
In this embodiment, in order to further illustrate the feasibility and effectiveness of the fault diagnosis method for a marine diesel generator based on integrated deep learning, in this embodiment, the most basic and most common support bearing in the marine diesel generator is taken as an example, and experimental data is obtained from a rolling bearing fault data set provided by the university of kesixi in united states, and the experimental bench is shown in fig. 6.
In this embodiment, the experimental bearing is used for supporting the motor spindle, and the bearing model is: 6205-2RS SKF deep groove ball bearings, wherein the experiment comprises 9 bearing fault types, namely single-point pits with the fault diameters of 0.18, 0.36 and 0.53mm are respectively implanted on an inner ring, an outer ring and balls of the bearings by adopting an electric spark finish machining technology; the experiment is that a unidirectional acceleration sensor is respectively arranged on a driving end, a fan end and a base of a motor and is used for measuring vibration signals of the motor in different fault states; the experiment included 4 load conditions, 0 horsepower (1797 r/min), 1 horsepower (1772 r/min), 2 horsepower (1750 r/min) and 3 horsepower (1730 r/min), respectively; the sampling frequency of all experiments is 12kHz, and the sampling time is 10 seconds, so that each type of faults respectively obtain a group of one-dimensional time sequence original data segments with the length L of 3 channels of 120000 points; thus, according to steps S101 and S102 of the present invention, a multi-channel one-dimensional time series data fusion dataset { D }, can be obtained Melting and melting And data set { D } Melting and melting Is of the shape [9,3,120000 ]]9 represents 9 failure types, 3 represents 3 sensor channels, and 120000 represents the data length.
Secondly, performing data truncation processing by adopting a sliding window sampling-based method to generate a standard two-dimensional feature map training sample total data set { T } 2D The method comprises the steps of carrying out a first treatment on the surface of the In the present embodiment, a sliding sampling window length is employedW num 500 sliding window step sizeS num 400 number of data points of input sampleI num 120000, so that the standard two-dimensional feature map training sample total data set { T } generated by the data truncation processing method based on the sliding window overlap sampling method can be calculated according to the method provided by the invention 2D Number of training samples in (a)T num 1196; in order to facilitate subsequent statistical analysis, in this embodiment, only 1000 training samples are randomly reserved after rounding, and the length of each training sample is generatedW num 500, therefore, a standard two-dimensional feature map trains the sample total dataset { T } 2D Is of the shape [9,3,1000,500 ]]Each training sample has a shape of [3,500 ]]As shown in table 1.
Thirdly, dividing a data set; training a standard two-dimensional feature map into a sample total dataset { T } 2D The 1000 samples in each health state type are randomly divided into a training set, a verification set and a test set, and the dividing method is as follows: first training a sample total dataset { T } from a standard two-dimensional feature map 2D The method comprises the steps that 30% of samples in 1000 fault samples in each health state type are randomly selected to serve as test sets, and 80% and 20% of samples in the rest 70% of samples are randomly selected to serve as training sets and verification sets respectively; namely: the 1000 samples of each health status type were taken as 30% of the test set (1000×0.3=300), 30% of the remaining 70% were taken as the validation set (1000×0.7×0.2=140), and the remaining 70% were taken as the training set (1000×0.7×0.8=560).
Table 1 fault data set for experimental bearings.
Fourth, in this embodiment, the constructed integrated deep learning space-time feature extraction fault diagnosis model is shown in fig. 4, and includes an input data fusion layer, an integrated deep learning space-time feature extraction layer and a Softmax discrimination output layer, where the integrated deep learning space-time feature extraction layer adopts a diagnosis model including 2 LSTM layers and 3 1D-CNN convolution layers, and the number of neurons of the LSTM layers is 64 and the number of neurons of the convolution layers of 32,1D-CNN layers is 64, 32 and 9; in the integrated deep learning space-time feature extraction fault diagnosis model established in the embodiment, firstly, a 2-layer LSTM module is adopted to extract time-related features and memory information from original [3,500] sample data through a unique network structure, then the extracted memory feature data is input into a 3-layer 1D-CNN network to extract micro fault features, then the micro fault features are input into a Softmax layer after dimension reduction and parameter reduction of a 1D-GAP layer to complete classification of 9 faults, and finally, the super parameters of the established integrated deep learning space-time feature extraction fault diagnosis model are shown in a table 2.
Table 2 establishes LSTM-GCNN diagnostic model hyper-parameters.
Fifth, initializing the integrated deep learning space-time feature to extract the parameter weight of the fault diagnosis model, and then adopting the total training set { T } Training device Training the integrated deep learning space-time feature extraction fault diagnosis model by using all two-dimensional feature map training samples, training and learning model parameters of each of the integrated deep learning space-time feature extraction layer and the deep feature extraction layer by repeatedly executing forward propagation and backward propagation iterative computation processes in the training process, and calling a total verification set { T }, in real time in the training process Verification Cross-verifying the model in the training process by all verification samples in the training process; after 100 times of iterative training, the training is finished, and the optimal parameter weight value of each network layer when the verification set accuracy is highest in the training process of the integrated deep learning space-time feature extraction fault diagnosis model is saved.
Finally, the total test set { T }, is used Measuring The two-dimensional feature map test sample or the new fault sample in the model is used for testing the trained integrated deep learning space-time feature extraction fault diagnosis model, and the fault diagnosis result is directly output, as shown in the table 3.
In order to compare and verify the superiority and feasibility of the established integrated deep learning space-time feature extraction fault diagnosis model, the embodiment carries out comparison analysis on the proposed integrated deep learning space-time feature extraction fault diagnosis model and a main stream deep full-connection BP neural network (DNN), a current CNN algorithm and a current LSTM algorithm; in table 3, the diagnostic results were evaluated using the accuracy, recall, and F1 mean.
Table 3 comparison of diagnostic results for different deep learning models.
As can be seen from comparison of table 3, among the above 5 algorithms, the method proposed by the present invention has more superior diagnostic performance, firstly, in terms of accuracy: DNN, CNN, LSTM, the average value of F1 is 94.74%, 98.96% and 98.51%, and the diagnosis accuracy of the method provided by the invention is up to 100%, which is obviously higher than that of the single CNN or LSTM algorithm, so that the designed algorithm can be illustrated to obtain higher feature extraction capability and fault recognition rate.
Secondly, in terms of training time: the 5 algorithms are LSTM, the proposed method, DNN and CNN from fast to slow in turn; notably, are: DNN and LSTM are faster than the proposed method in terms of test time, but the accuracy of the DNN and LSTM is lower, especially the DNN algorithm is only 94.74%, and the misdiagnosis rate is higher; although the CNN obtains higher accuracy, the training time of the algorithm is longest than that of other 4 algorithms, which is up to 1000 seconds or less, and the test time is too long, which is not beneficial to the rapid diagnosis of micro faults. However, the proposed algorithm only takes 0.797 seconds to test 2700 samples, and the test speed can meet the real-time requirement of mechanical fault diagnosis because the evolution of mechanical faults is a slow-developing process.
The application results of the embodiment show that the method provided by the invention obtains excellent diagnosis accuracy; according to the invention, any manual feature extraction operation is not needed, an operator is not required to master complex multiple advanced signal processing technologies, the whole diagnosis process is automatically completed without manual intervention, intelligent, efficient and quick data fusion, time-associated feature information mining, depth feature extraction and automatic identification and diagnosis of micro faults can be automatically carried out on multi-sensor one-dimensional time sequence data acquired by the ship diesel generator under the time-varying rotating speed working condition, and the method has better universality and operability, so that fault diagnosis technicians can more intelligently, conveniently and quickly diagnose the micro faults of the ship diesel generator under the time-varying rotating speed working condition.
It should also be noted that the above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and those skilled in the art may make routine obvious small modifications or small combinations based on the present invention, so long as the technical content included in the content described in the present invention is within the scope of the present invention.
Claims (4)
1. The fault diagnosis method of the marine diesel generator based on the integrated deep learning is characterized by comprising the following steps of:
(1) Collecting original fault data of a multichannel one-dimensional time sequence of a ship diesel generator;
arranging sensors on the ship diesel generator, acquiring one-dimensional time sequence original fault data generated when the ship diesel generator operates in various health states by using the sensors, wherein the number of the sensors is M (namely M channels), the data acquired by each sensor is a continuous one-dimensional time sequence original long data segment, and the sample length of the one-dimensional time sequence original long data segment is L, namely the one-dimensional time sequence original long data segment comprises L data points; the health states of the ship diesel generator are set to be N health states, wherein the health states comprise a normal state and N-1 fault states, and each health state comprises one-dimensional time sequence original fault long data segments of M channels;
(2) Data fusion is carried out on the original fault data of the multichannel one-dimensional time sequence, and a multichannel one-dimensional time sequence data fusion data set { D } Melting and melting ；
Using the number of monitoring of the N health states Multi-channel one-dimensional time series data fusion dataset { D }, constructed for training and testing of deep learning diagnostic models Melting and melting The multi-channel one-dimensional time series data fusion dataset { D } Melting and melting Is configured to include N subsets: { D } Melting and melting ={D 1 ,D 2 , ...,D i , ...,D N } Melting and melting Corresponding to N health states, each subset { D } i } Melting and melting Each of which comprises M one-dimensional time series original long data segments with length L, each subset { D i } Melting and melting Namely, the shape after data fusion is [ M, L ]]The method for data fusion is set as follows: a 1 st sensor channel is a one-dimensional time series original long data segment with the length L, a 2 nd sensor channel is a one-dimensional time series original long data segment with the length L, …, and an M sensor channel is a one-dimensional time series original long data segment with the length L; multi-channel one-dimensional time sequence data fusion data set { D }, obtained under N health states Melting and melting The multi-dimensional tensor matrix shape is [ N, M, L]The method comprises the following steps: the length of each one-dimensional time sequence original data segment of M sensor channels under N health states is L data points;
(3) Data truncation processing is carried out based on a sliding window overlapping sampling method, and a standard two-dimensional feature map training sample total data set { T }, is generated 2D ；
Fusing the acquired multichannel one-dimensional time series data into a data set { D } Melting and melting The shape of each health state is [ M, L ]]The multi-channel one-dimensional time sequence long data segment adopts a sliding window overlapping sampling method to carry out data truncation processing, and the sliding window overlapping sampling method is set to adopt a fixed sliding windowW num The shape after each data fusion is [ M, L ]]Is a two-dimensional matrix feature map { D ] i } Melting and melting With fixed sliding step lengthS num Performing sliding sampling along the time axis direction, wherein the length of the sliding window is as followsW num Width is M, the sliding windowW num Generating and saving all data points in the sliding window as a standard every time one step is movedTwo-dimensional feature map training sample {tA long multi-channel one-dimensional time series data fusion data set { D }, with a length L Melting and melting GeneratingT num Length of isW num Training a sample total dataset { T }, for a short standard two-dimensional feature map 2D Standard two-dimensional feature map training sample total dataset { T } 2D The shape of (C) is [ N, M,T num ,W num ]；
the data truncation processing method based on the sliding window overlap sampling method is generatedT num Standard two-dimensional feature map training sample total data set { T } of short time sequence 2D In (a) and (b)T num The calculation method of (1) is as follows: T num =CEILING[（I num -W num +1）/S num ,1]Wherein, the method comprises the steps of, wherein,I num representing the number of data points of the input sample,W num representing the length of the sliding window, i.e. the length of the generated standard two-dimensional feature map training samples,S num representing the step size of the sliding window,T num representing the number of new fault samples, wherein CEILING is a rounding function, namely the decimal point of the calculation result in the middle brackets is set to be rounded to 1;
the data truncation processing method based on the sliding window overlap sampling method is based on the sliding window step lengthS num Is provided with three standard two-dimensional characteristic diagram training sample generation modes, when the sliding window step length is adoptedS num Equal to the length of the sliding windowW num Non-overlapping aliquoting for aliquoting and intercepting when sliding window step lengthS num Length greater than the sliding windowW num At intervals of sparse interception, when the sliding window step lengthS num Less than the length of the sliding windowW num Incremental overlapping sampling for sample expansion;
(4) Dividing a data set;
training a standard two-dimensional feature map into a sample total dataset { T } 2D Each of the health status typesT num The method for dividing the samples into a training set, a verification set and a test set comprises the following steps: first, the standard two-dimensionalFeature map training sample total dataset { T } 2D Each of the health status typesT num Randomly selecting 30% of the number of samples from the fault samples as a test set, randomly taking out 60% of the remaining 70% of samples to be classified as a training set and 40% of the samples as a verification set; finally, training the standard two-dimensional feature map to obtain a total data set { T }, and 2D All training set samples in N health states in (1) form a total training set { T } Training device Training a standard two-dimensional feature map into a sample total dataset { T } 2D All validation set samples in the N classes of health states constitute a total validation set { T } Verification Training a standard two-dimensional feature map into a sample total dataset { T } 2D All test set samples of N types of health status constitute a total test set { T } Measuring ；
(5) Constructing an integrated deep learning space-time feature extraction fault diagnosis model;
the integrated deep learning space-time feature extraction fault diagnosis model comprises an input data fusion layer, an integrated deep learning space-time feature extraction layer and a Softmax discrimination output layer; the integrated deep learning space-time feature extraction layer is arranged to comprise a long-short-time memory cyclic neural network Layer (LSTM) of a P layer, a one-dimensional convolutional neural network layer (1D-CNN) of a Q layer and a one-dimensional global average pooling layer (1D-GAP) layer, wherein the LSTM layer is used for extracting time-associated memory features from training samples, the one-dimensional convolutional neural network layer is used for extracting depth representative features from received data, and the 1D-GAP layer is used for carrying out data compression and parameter quantity reduction on the received data;
The one-dimensional convolutional neural network layer consists of a one-dimensional convolutional layer, a Relu activation layer, a Dropout layer and a one-dimensional convolutional layer;
the number of layers P of the long-short-time memory circulating neural network layer in the integrated deep learning space-time feature extraction layer is set to be 2-5; the number of layers of the one-dimensional convolutional neural network layer is set to be 2-5;
the input data fusion layer, the integrated deep learning space-time feature extraction layer and the Softmax discrimination output layer are sequentially connected in series, and firstly, the input data fusion layer is used for acquiring a multichannel one-dimensional time sequenceThe original monitoring data are subjected to data fusion and a standard two-dimensional feature map training sample total data set { T }, is generated 2D Total training set { T } to be generated after data division Training device Total validation set { T } Verification Sum total test set { T } Measuring Outputting the standard two-dimensional feature map training samples of the (1) to an integrated deep learning space-time feature extraction layer; secondly, training a sample total data set { T } of the standard two-dimensional feature map by integrating a plurality of LSTM networks and 1D-CNN networks in the deep learning space-time feature extraction layer 2D Performing feature mining on each sample in the 1D-CNN layer, extracting fault features of N health states of the ship diesel generator, and performing feature compression and parameter quantity reduction on high-dimensional feature data output by the 1D-CNN layer by adopting a one-dimensional global average pooling layer, so that the training speed of the model is further improved, and the waiting time of diagnostic test is reduced; finally, the low-dimensional characteristic sequence data output by the one-dimensional global averaging layer is output to the Softmax discrimination output layer for normalization processing, and the Softmax discrimination output layer outputs a final accuracy result;
(6) Initializing integrated deep learning space-time characteristics to extract parameter weights of a fault diagnosis model;
(7) Using the total training set { T } Training device Training the integrated deep learning space-time feature extraction fault diagnosis model by using the standard two-dimensional feature map training samples; performing super-parameter adjustment on the integrated deep learning space-time feature extraction fault diagnosis model to obtain optimal model super-parameters of an integrated deep learning space-time feature extraction layer in the integrated deep learning space-time feature extraction fault diagnosis model; the training process of the integrated deep learning space-time feature extraction fault diagnosis model is set to train and learn model parameters of each of the integrated deep learning space-time feature extraction layer and the deep feature extraction layer by repeatedly executing forward propagation and backward propagation iterative computation processes, and the total verification set { T } is called in real time in the training process Verification Cross-verifying the diagnosis model in the training process by the verification sample in the process to judge whether the training of the integrated deep learning space-time feature extraction fault diagnosis model is fitted or not;
the judgment is integrated with the deep learningThe method for judging whether the empty feature extraction fault diagnosis model training process is subjected to fitting or not is as follows: when the total training set { T } Training device Sum total validation set { T } Verification The accuracy of the samples of (2) increases with the number of training rounds and the total training set { T }, is increased Training device Accuracy of training samples of (1) and total validation set { T } Verification When the accuracy rates of the verification samples are close to and consistent with each other, the model parameter training is normal, and the step (7) is continuously executed for training; when the total training set { T } Training device Sum total validation set { T } Verification The accuracy of the samples of (1) increases with the number of training rounds, but the total validation set { T } Verification The accuracy of the validation samples of (1) starts to be lower than the total training set { T } Training device When the accuracy of the training samples of the integrated deep learning space-time feature extraction fault diagnosis model is improved, the model training is stopped immediately, the step (5) is skipped, the super parameters of the integrated deep learning space-time feature extraction fault diagnosis model are modified again, the method is repeatedly executed until the integrated deep learning space-time feature extraction fault diagnosis model is not subjected to fitting and the verification set accuracy reaches a set target value or iteration number, the model training is finished, and meanwhile, the optimal parameter weight value of each network layer when the verification set accuracy in the integrated deep learning space-time feature extraction fault diagnosis model training process is highest is saved;
(8) Using the total test set { T } Measuring The standard two-dimensional feature map test sample or the new fault sample tests the trained integrated deep learning space-time feature extraction fault diagnosis model, and directly outputs a fault diagnosis result.
2. The integrated deep learning based marine diesel generator fault diagnosis method according to claim 1, wherein the sensor type is set as a vibration acceleration sensor.
3. The integrated deep learning based marine diesel generator fault diagnosis method according to claim 1, wherein the sensor further comprises one or more of a noise sensor, a pressure sensor, a displacement sensor, a rotation speed sensor, a voltage sensor, a current sensor and other common fault monitoring sensors.
4. The integrated deep learning based marine diesel generator fault diagnosis method according to claim 1 or 2, wherein the different types and numbers of sensors for marine diesel generator data acquisition are set to have the same sampling frequency.
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