Mechanical fault diagnosis method based on multi-sensor information fusion migration network

Abstract

The invention discloses a mechanical fault diagnosis method based on a multi-sensor information fusion migration network, which includes firstly collecting multi-sensor data, obtaining multiple source domain data sets and target domain data sets, and then constructing a network diagnosis suitable for multi-sensor information fusion migration Model, which has a feature sharing layer and M convolutional neural networks; then constructs the loss function of each convolutional neural network; then performs multi-sensor information fusion migration network diagnostic model training, based on M source domain data sets and target The target domain training data of the domain dataset, in each iteration, train the first to the Mth network sequentially according to the order of the source domain sensors until the number of iterations or classification accuracy is reached; finally, the target domain test data of the target domain dataset Input into the model, after the model and loss function processing and the weighted average of M outputs, the final classification and diagnosis results are obtained. The invention can effectively improve the precision of mechanical fault diagnosis.

Description

technical field

[0001] The invention relates to the technical field of mechanical fault diagnosis, in particular to a mechanical fault diagnosis method based on multi-sensor information fusion migration network. Background technique

[0002] The structure of the mechanical system is complex and there are many parts. In order to monitor the status of the mechanical system, multiple sensors are usually installed at different positions of the key components of the system to obtain information of different sensitivity and complementarity. However, existing methods usually only use the information of a single sensor for fault diagnosis, and the reliability of diagnosis is affected by various factors such as sensor installation location, signal transmission path, and variable working conditions. How to effectively use and fuse multi-sensor information to improve the classification accuracy and diagnostic reliability of mechanical fault diagnosis models is a difficult problem in th...

Examples

Embodiment 1

[0081] This embodiment discloses a mechanical fault diagnosis method based on a multi-sensor information fusion migration network. The method uses a feature sharing layer and a plurality of convolutional neural networks to perform a diagnosis based on the historical data of multiple source domain sensors in the source domain diagnosis task. learning, and transfer the knowledge learned on the labeled source domain dataset to the unlabeled target domain task, improving the diagnostic performance of the target domain task. Such as figure 1 As shown, the steps are as follows:

[0082] S1. Multi-sensor data acquisition: According to the actual operating conditions of mechanical equipment, arrange source domain sensors for collecting fault data at different positions of mechanical equipment, and then use source domain sensors to obtain fault data with different sensitivities to faults at each position, Thereby forming multiple source domain datasets Among them, the source domain da...

Embodiment 2

[0136] This embodiment discloses a mechanical fault diagnosis device based on multi-sensor information fusion migration network, such as Figure 5 shown, including:

[0137] The source domain sensor data acquisition module is used to use the source domain sensors arranged at different positions of the mechanical equipment according to the actual operating conditions of the mechanical equipment to obtain fault data with different sensitivities to faults at each position, thereby forming multiple source domain data sets Among them, the source domain dataset consists of source domain sensor data x s and its corresponding label y s Composition, the label corresponds to the health status of the source domain diagnosis task, M represents the number of source domain sensors, and also represents the number of source domain data sets, j represents the serial number of the source domain sensor, and also represents the serial number of the source domain data set;

[0138] The target d...

Embodiment 3

[0159] This embodiment discloses a storage medium, which stores a program. When the program is executed by a processor, the method for diagnosing mechanical faults based on the multi-sensor information fusion migration network described in Embodiment 1 is implemented, specifically as follows:

[0160] S1. Multi-sensor data acquisition: According to the actual operating conditions of mechanical equipment, arrange source domain sensors for collecting fault data at different positions of mechanical equipment, and then use source domain sensors to obtain fault data with different sensitivities to faults at each position, Thereby forming multiple source domain datasets Among them, the source domain dataset consists of source domain sensor data x s and its corresponding label y s Composition, the label corresponds to the health status of the source domain diagnosis task, M represents the number of source domain sensors, and also represents the number of source domain data sets, j ...

technical field

[0001] The invention relates to the technical field of mechanical fault diagnosis, in particular to a mechanical fault diagnosis method based on multi-sensor information fusion migration network. Background technique

[0002] The structure of the mechanical system is complex and there are many parts. In order to monitor the status of the mechanical system, multiple sensors are usually installed at different positions of the key components of the system to obtain information of different sensitivity and complementarity. However, existing methods usually only use the information of a single sensor for fault diagnosis, and the reliability of diagnosis is affected by various factors such as sensor installation location, signal transmission path, and variable working conditions. How to effectively use and fuse multi-sensor information to improve the classification accuracy and diagnostic reliability of mechanical fault diagnosis models is a difficult problem in th...

Examples

Embodiment 1

[0081] This embodiment discloses a mechanical fault diagnosis method based on a multi-sensor information fusion migration network. The method uses a feature sharing layer and a plurality of convolutional neural networks to perform a diagnosis based on the historical data of multiple source domain sensors in the source domain diagnosis task. learning, and transfer the knowledge learned on the labeled source domain dataset to the unlabeled target domain task, improving the diagnostic performance of the target domain task. Such as figure 1 As shown, the steps are as follows:

[0082] S1. Multi-sensor data acquisition: According to the actual operating conditions of mechanical equipment, arrange source domain sensors for collecting fault data at different positions of mechanical equipment, and then use source domain sensors to obtain fault data with different sensitivities to faults at each position, Thereby forming multiple source domain datasets Among them, the source domain da...

Embodiment 2

[0136] This embodiment discloses a mechanical fault diagnosis device based on multi-sensor information fusion migration network, such as Figure 5 shown, including:

[0137] The source domain sensor data acquisition module is used to use the source domain sensors arranged at different positions of the mechanical equipment according to the actual operating conditions of the mechanical equipment to obtain fault data with different sensitivities to faults at each position, thereby forming multiple source domain data sets Among them, the source domain dataset consists of source domain sensor data x s and its corresponding label y s Composition, the label corresponds to the health status of the source domain diagnosis task, M represents the number of source domain sensors, and also represents the number of source domain data sets, j represents the serial number of the source domain sensor, and also represents the serial number of the source domain data set;

[0138] The target d...

Embodiment 3

[0159] This embodiment discloses a storage medium, which stores a program. When the program is executed by a processor, the method for diagnosing mechanical faults based on the multi-sensor information fusion migration network described in Embodiment 1 is implemented, specifically as follows:

[0160] S1. Multi-sensor data acquisition: According to the actual operating conditions of mechanical equipment, arrange source domain sensors for collecting fault data at different positions of mechanical equipment, and then use source domain sensors to obtain fault data with different sensitivities to faults at each position, Thereby forming multiple source domain datasets Among them, the source domain dataset consists of source domain sensor data x s and its corresponding label y s Composition, the label corresponds to the health status of the source domain diagnosis task, M represents the number of source domain sensors, and also represents the number of source domain data sets, j ...

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