CN111076927A - Gear fault diagnosis method, device and system based on deep neural network - Google Patents
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Abstract
The invention relates to the technical field of fault diagnosis, in particular to a gear fault diagnosis method, a gear fault diagnosis device and a gear fault diagnosis system based on a deep neural network, wherein the method comprises the following steps: firstly, acquiring test data acquired by a sensor, wherein the test data comprises a torque signal, a vibration signal and a rotating speed signal of a gear; and then, performing semantic automatic diagnosis on the test data by adopting a gear fault diagnosis model to obtain a fault diagnosis result of the gear, wherein the fault diagnosis result comprises whether the gear is in fault or not and the fault type.
Description
Technical Field
The invention relates to the technical field of fault diagnosis, in particular to a gear fault diagnosis method, device and system based on a deep neural network.
Background
The gear is a mechanical element with a gear on a wheel rim which is continuously engaged to transmit motion and power, and the defects of cracks, inclusions, air holes and the like are easy to appear in the gear. The stability of gear operation plays an important role in machining, and gear failure causes huge economic loss, so that the performance and reliability of gear products have important values.
In order to improve the performance and reliability of gear products, a gear defect detection method is urgently needed to be researched, various types of affected gears are detected, and automatic diagnosis of gear faults is realized.
Disclosure of Invention
In order to solve the problems, the invention provides a gear fault diagnosis method, device and system based on a deep neural network, which can realize automatic diagnosis of gear faults.
In order to achieve the purpose, the invention provides the following technical scheme:
according to the embodiment of the first aspect of the invention, a gear fault diagnosis method based on a deep neural network is provided, which comprises the following steps:
acquiring test data of a gear, wherein the test data of the gear comprises: a torque signal, a vibration signal and a rotating speed signal of the gear;
and performing semantic automatic diagnosis on the test data by adopting a gear fault diagnosis model to obtain a fault diagnosis result of the gear, wherein the fault diagnosis result comprises whether a fault exists and a fault category.
Preferably, the semantic automatic diagnosis is performed on the test data by using the gear fault diagnosis model to obtain a fault diagnosis result of the gear, and the method specifically comprises the following steps:
acquiring sample signals acquired by a sensor, wherein the sample signals comprise a torque signal, a vibration signal and a rotating speed signal of a gear;
converting the sample signal from an analog signal to a digital signal, and performing normalization processing to obtain normalized data;
inputting the normalized data into an RBF neural network for training to obtain a gear fault diagnosis model;
and inputting the test data of the gear into the gear fault diagnosis model to obtain a fault diagnosis result of the gear.
Further, before converting the sample signal from an analog signal to a digital signal, the method further includes:
acquiring gear states corresponding to the sample signals, wherein the gear states comprise a normal state and a fault state, and the fault state comprises gear fracture, gear abrasion and tooth surface scratch;
and classifying and labeling the normalized data according to the gear state corresponding to the sample signal.
Preferably, the RBF neural network consists of 3 parts of an input layer, a hidden layer and an output layer, wherein an activation function of the hidden layer is composed of a gaussian function.
Preferably, the inputting the normalized data into the RBF neural network for training to obtain the gear fault diagnosis model includes:
clustering the normalized data according to the gear state;
calculating the clustering center and the mean square error of each cluster;
and calculating the weight from the hidden layer to the output layer, and adjusting the weight of the output layer.
Preferably, the value range of the normalized data is (0, 1).
According to a second aspect of the present invention, there is provided a gear fault diagnosis apparatus based on a deep neural network, including:
the data acquisition module is used for acquiring the test data of the gear, and the test data of the gear comprises: a torque signal, a vibration signal and a rotating speed signal of the gear;
and the fault diagnosis module is used for performing semantic automatic diagnosis on the test data by adopting a gear fault diagnosis model to obtain a fault diagnosis result of the gear, wherein the fault diagnosis result comprises whether a fault exists and a fault category.
Preferably, the fault diagnosis module is specifically configured to:
acquiring sample signals acquired by a sensor, wherein the sample signals comprise a torque signal, a vibration signal and a rotating speed signal of a gear;
converting the sample signal from an analog signal to a digital signal, and performing normalization processing to obtain normalized data;
inputting the normalized data into an RBF neural network for training to obtain a gear fault diagnosis model;
and inputting the test data of the gear into the gear fault diagnosis model to obtain a fault diagnosis result of the gear.
Further, the fault diagnosis module is further configured to:
acquiring gear states corresponding to the sample signals, wherein the gear states comprise a normal state and a fault state, and the fault state comprises gear fracture, gear abrasion and tooth surface scratch;
and classifying and labeling the normalized data according to the gear state corresponding to the sample signal.
According to the third aspect embodiment of the invention, a gear fault diagnosis system based on a deep neural network is provided, the system comprises: the gear fault diagnosis program is stored on the memory and can be operated on the processor, and when being executed by the processor, the gear fault diagnosis program realizes the steps of the gear fault diagnosis method based on the deep neural network, which is provided by the embodiment of the first aspect of the invention.
The invention has the beneficial effects that: the invention discloses a gear fault diagnosis method, a gear fault diagnosis device and a gear fault diagnosis system based on a deep neural network, wherein the method comprises the following steps: firstly, acquiring test data acquired by a sensor, wherein the test data comprises a torque signal, a vibration signal and a rotating speed signal of a gear; and then, carrying out semantic automatic diagnosis on the test data by adopting a gear fault diagnosis model to obtain a fault diagnosis result of the gear, wherein the fault diagnosis result comprises whether a fault exists and a fault category. The invention also correspondingly provides a gear fault diagnosis device and system based on the deep neural network, and the automatic diagnosis of the gear fault can be realized.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a gear fault diagnosis method based on a deep neural network according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating step S200 in FIG. 1 according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an RBF neural network according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a gear fault diagnosis device based on a deep neural network according to an embodiment of the present invention.
Detailed Description
The conception, specific structure and technical effects of the present disclosure will be clearly and completely described below in conjunction with the embodiments and the accompanying drawings to fully understand the objects, aspects and effects of the present disclosure. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Referring to fig. 1, fig. 1 shows a gear fault diagnosis method based on a deep neural network, including the following steps:
step S100, obtaining test data of the gear, wherein the test data of the gear comprises the following steps: a torque signal, a vibration signal and a rotational speed signal of the gear.
And S200, performing semantic automatic diagnosis on the test data by adopting a gear fault diagnosis model to obtain a fault diagnosis result of the gear. Wherein the fault diagnosis result comprises whether a fault occurs and a fault category.
The torsional vibration phenomenon of the shafting can be caused by the influences of external disturbance, electric shock, electromechanical system coupling, sudden changes of mechanical load and power system working conditions, or various transient operation (such as starting and stopping) processes and the like. Torsional vibration caused by transient impact and transient process is short in duration, caused vibration and noise are small, the torsional vibration and the noise are not easy to feel on site, but torsional fatigue phenomena can occur on a transmission shaft and a gear which often generate torsional vibration, micro cracks are formed on the surface of the gear, the cracks are gradually deepened in the torsional vibration of one time and the other time, and finally the transmission shaft is broken and fails. In the prior art, gear signal detection technology is mature, and the method can be divided into a contact measurement method and a non-contact measurement method according to the installation and use mode of a test sensor. The former is a measurement mode that a sensor (strain rosette, accelerometer, etc.) is arranged at the shaft end, and an output signal of the sensor is sent to a signal receiving device through a radio or a current collecting ring; the latter often measure torsional vibrations by means of a gear, bolt boss, code disc or other equivalent structure on the shaft by means of an eddy current sensor or hall sensor generating a change in the electromagnetic field and subsequently inducing a signal.
In the embodiment, the test data is acquired through the sensor so as to acquire a torque signal, a vibration signal and a rotating speed signal of the gear; and then, performing semantic automatic diagnosis on the test data by adopting a gear fault diagnosis model to obtain a fault diagnosis result of the gear, wherein the fault diagnosis result comprises whether the gear is in fault or not and the fault type, so that the automatic diagnosis of the gear fault is realized.
Referring to fig. 2, in a modified embodiment, step S200 is specifically:
step S210, acquiring sample signals collected by a sensor, wherein the sample signals comprise a torque signal, a vibration signal and a rotating speed signal of a gear;
step S220, converting the sample signal from an analog signal to a digital signal, and performing normalization processing to obtain normalized data;
step S230, inputting the normalized data into an RBF neural network for training to obtain a gear fault diagnosis model;
and S240, inputting the test data of the gear into a gear fault diagnosis model to obtain a fault diagnosis result of the gear.
In the embodiment, the sample signals acquired by the sensor need to be subjected to digitization processing and normalization processing, so that subsequent training is facilitated, the digitization processing is sampling, quantification and analog-to-digital conversion, and the normalization processing is to convert all digital signals into a digital range interval in an equal proportion;
the Radial Basis Function (RBF) neural network adopted in this embodiment is a forward network constructed based on a Function approximation theory, and learning of this network is equivalent to finding a best fit plane of training data in a multidimensional space. Each hidden layer neuron activation function of the RBF neural network forms a basis function of a fitting plane, and the RBF neural network is a local approximation network, namely only a few neurons exist in a certain local area of an input space for determining the output of the network.
In a modified embodiment, before the step S220, the method further includes:
acquiring gear states corresponding to the sample signals, wherein the gear states comprise a normal state and a fault state, and the fault state comprises gear fracture, gear abrasion and tooth surface scratch;
and classifying and labeling the normalized data according to the gear state corresponding to the sample signal.
Therefore, each normalized data corresponds to one gear state, so that the clustered gear states can be obtained, and the mapping from sample data to the gear states is established through subsequent training.
In a modified embodiment, the RBF neural network consists of 3 parts of an input layer, an implicit layer and an output layer, wherein an activation function of the implicit layer is formed by a Gaussian function.
Fig. 3 is a structural diagram of an RBF neural network. Where xi (i ═ 1, 2, …, n) is the input of the input layer, Ri (i ═ 1, 2, …, p) is the output of the hidden layer, and yi (i ═ 1, 2, …, m) is the output of the output layer. The input layer node only transmits an input signal to the hidden layer, the activation function of the hidden layer node is formed by a Gaussian function, in the RBF neural network, the mapping from the input to the hidden layer is nonlinear, and the function from the hidden layer to the output layer node is a linear function.
In a modified embodiment, the step S230 includes:
clustering the normalized data according to the gear state;
calculating the clustering center and the mean square error of each cluster by adopting a mean value method;
and calculating the weight from the hidden layer to the output layer by adopting an error correction learning algorithm, and adjusting the weight of the output layer by adopting a minimum mean square rule.
In this embodiment, the normalized data is labeled with the gear states, the normalized data in the same gear state is used as a class, so as to cluster the normalized data, determine p cluster centers, where p is a natural number, and calculate the cluster center of each cluster by using a mean value method, so that each cluster has one cluster center, and determine the boundary of each cluster by calculating the mean square error of each cluster.
In this embodiment, the sample data is used as a training data set to establish a fault prediction model and generate a prediction rule. After the sample to be predicted is input, the trained prediction model can be used for marking the gear state of the sample to be predicted.
In a modified embodiment, the normalized data has a value range of (0, 1).
Referring to fig. 4, an embodiment of the present invention further provides a gear fault diagnosis apparatus based on a deep neural network, including:
the data acquisition module 100 is used for acquiring test data acquired by the sensor, wherein the test data comprises a torque signal, a vibration signal and a rotating speed signal of the gear;
and the fault diagnosis module 200 is configured to perform semantic automatic diagnosis on the test data by using a gear fault diagnosis model to obtain a fault diagnosis result of the gear, where the fault diagnosis result includes whether there is a fault and a fault category.
Preferably, the fault diagnosis module 200 is specifically configured to:
acquiring sample signals acquired by a sensor, wherein the sample signals comprise a torque signal, a vibration signal and a rotating speed signal of a gear;
converting the sample signal from an analog signal to a digital signal, and performing normalization processing to obtain normalized data;
inputting the normalized data into an RBF neural network for training to obtain a gear fault diagnosis model;
and inputting the test data acquired by the sensor into the gear fault diagnosis model to obtain a fault diagnosis result of the gear.
Further, the fault diagnosis module 200 is further configured to:
acquiring gear states corresponding to the sample signals, wherein the gear states comprise a normal state and a fault state, and the fault state comprises gear fracture, gear abrasion and tooth surface scratch;
and classifying and labeling the normalized data according to the gear state corresponding to the sample signal.
The embodiment of the invention also provides a gear fault diagnosis system based on the deep neural network, which comprises: the gear fault diagnosis method comprises a memory, a processor and a gear fault diagnosis program which is stored on the memory and can run on the processor, wherein the gear fault diagnosis program realizes the steps of the gear fault diagnosis method based on the deep neural network provided by the embodiment when being executed by the processor.
It can be seen that the contents in the foregoing method embodiments are all applicable to this system embodiment, the functions specifically implemented by this system embodiment are the same as those in the foregoing method embodiment, and the advantageous effects achieved by this system embodiment are also the same as those achieved by the foregoing method embodiment.
An embodiment of the present invention further provides a storage medium, where the computer readable storage medium stores a gear fault diagnosis program, and the gear fault diagnosis program, when executed by a processor, implements the steps of the gear fault diagnosis method based on the deep neural network described above.
Through the above description of the embodiments, it is clear to those skilled in the art that the method of the above embodiments may be implemented by software, and the embedded software is loaded into a processor, so as to effectively utilize data collected by various sensors to perform gear fault diagnosis. Based on this understanding, the technical solutions of the present invention may be embodied in the form of software products, which essentially or partially contribute to the prior art.
The Processor may be a Central-Processing Unit (CPU), other general-purpose Processor, a Digital Signal Processor (DSP), an Application-Specific-Integrated-Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. The general purpose processor can be a microprocessor or the processor can be any conventional processor and the like, the processor is a control center of the gear fault diagnosis system based on the deep neural network, and various interfaces and lines are utilized to connect various parts of the whole gear fault diagnosis system based on the deep neural network.
The memory may be used to store the computer programs and/or modules, and the processor may implement various functions of the gear fault diagnosis system based on the deep neural network by operating or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory may primarily include a program storage area and a data storage area, which may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart-Media-Card (SMC), a Secure-Digital (SD) Card, a Flash-memory Card (Flash-Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
While the present disclosure has been described in considerable detail and with particular reference to a few illustrative embodiments thereof, it is not intended to be limited to any such details or embodiments or any particular embodiments, but it is to be construed with references to the appended claims so as to provide a broad, possibly open interpretation of such claims in view of the prior art, and to effectively encompass the intended scope of the disclosure. Furthermore, the foregoing describes the disclosure in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial modifications of the disclosure, not presently foreseen, may nonetheless represent equivalent modifications thereto.
Claims (10)
1. A gear fault diagnosis method based on a deep neural network is characterized by comprising the following steps:
acquiring test data of a gear, wherein the test data of the gear comprises: a torque signal, a vibration signal and a rotating speed signal of the gear;
and performing semantic automatic diagnosis on the test data by adopting a gear fault diagnosis model to obtain a fault diagnosis result of the gear, wherein the fault diagnosis result comprises whether a fault exists and a fault category.
2. The gear fault diagnosis method based on the deep neural network as claimed in claim 1, wherein the semantic automatic diagnosis is performed on the test data by using the gear fault diagnosis model to obtain a fault diagnosis result of the gear, and specifically, the method comprises the following steps:
acquiring sample signals acquired by a sensor, wherein the sample signals comprise a torque signal, a vibration signal and a rotating speed signal of a gear;
converting the sample signal from an analog signal to a digital signal, and performing normalization processing to obtain normalized data;
inputting the normalized data into an RBF neural network for training to obtain a gear fault diagnosis model;
and inputting the test data of the gear into the gear fault diagnosis model to obtain a fault diagnosis result of the gear.
3. The gear fault diagnosis method based on the deep neural network as claimed in claim 2, wherein before converting the sample signal from an analog signal to a digital signal, the method further comprises:
acquiring gear states corresponding to the sample signals, wherein the gear states comprise a normal state and a fault state, and the fault state comprises gear fracture, gear abrasion and tooth surface scratch;
and classifying and labeling the normalized data according to the gear state corresponding to the sample signal.
4. The gear fault diagnosis method based on the deep neural network is characterized in that the RBF neural network consists of 3 parts of an input layer, an implicit layer and an output layer, wherein an activation function of the implicit layer is composed of a Gaussian function.
5. The method for diagnosing the gear fault based on the deep neural network as claimed in claim 4, wherein the inputting of the normalized data into the RBF neural network for training to obtain the gear fault diagnosis model comprises:
clustering the normalized data according to the gear state;
calculating the clustering center and the mean square error of each cluster;
and calculating the weight from the hidden layer to the output layer, and adjusting the weight of the output layer.
6. The deep neural network-based gear fault diagnosis method according to claim 2, wherein the value range of the normalized data is (0, 1).
7. A gear fault diagnosis device based on a deep neural network is characterized by comprising:
the data acquisition module is used for acquiring test data acquired by the sensor, wherein the test data acquired by the sensor comprises a torque signal, a vibration signal and a rotating speed signal of the gear;
and the fault diagnosis module is used for performing semantic automatic diagnosis on the test data by adopting a gear fault diagnosis model to obtain a fault diagnosis result of the gear, wherein the fault diagnosis result comprises whether a fault exists and a fault category.
8. The deep neural network-based gear fault diagnosis device according to claim 7, wherein the fault diagnosis module is specifically configured to:
acquiring sample signals acquired by a sensor, wherein the sample signals comprise a torque signal, a vibration signal and a rotating speed signal of a gear;
converting the sample signal from an analog signal to a digital signal, and performing normalization processing to obtain normalized data;
inputting the normalized data into an RBF neural network for training to obtain a gear fault diagnosis model;
and inputting the test data acquired by the sensor into the gear fault diagnosis model to obtain a fault diagnosis result of the gear.
9. The deep neural network-based gear fault diagnosis device according to claim 8, wherein the fault diagnosis module is further configured to:
acquiring gear states corresponding to the sample signals, wherein the gear states comprise a normal state and a fault state, and the fault state comprises gear fracture, gear abrasion and tooth surface scratch;
and classifying and labeling the normalized data according to the gear state corresponding to the sample signal.
10. A gear fault diagnosis system based on a deep neural network, the system comprising: a memory, a processor and a gear fault diagnosis program stored on the memory and executable on the processor, the gear fault diagnosis program when executed by the processor implementing the steps of the deep neural network based gear fault diagnosis method of any one of claims 1 to 6.
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Application publication date: 20200428 |