Information Fusion Method for Fault Diagnosis

At present, the information fusion methods of fault diagnosis are widely used according to the different fusion algorithms: Bayesian theorem information fusion fault diagnosis method; fuzzy information fusion fault diagnosis method; Dempster-Shafer (DS) evidence theory information fusion fault diagnosis method Diagnosis method; neural network information fusion fault diagnosis method; integrated information fusion fault diagnosis method, etc. The multi-sensor data fusion method based on deep learning is a very popular research direction in recent years, with good fusion effect and excellent applicability. Among them, a typical method is to use the combination of convolutional neural network (CNN) and recurrent neural network (RNN), which can effectively solve the problem of multi-sensor data fusion. Specifically, this method sends the data collected by different sensors to different CNNs for feature extraction, and then sends the extracted features to the RNN for serialization processing, thereby realizing the communication between different data sources. Effective fusion. The main advantages of this method are as follows: it can effectively provide multi-source data processing capabilities with time series properties, so it is suitable for complex occasions such as video processing and speech recognition. It can effectively deal with heterogeneous data problems such as different types of sensors, precision, and different sampling rates. The deep learning algorithm is used to adaptively learn the correlation between sensors, which has good adaptability and robustness. It can provide high-quality real-time feedback on multi-source data, and realize joint optimization of multiple sensors to improve the accuracy of fusion results. However, this method requires a large amount of data and computing resources, and the model is relatively complex, which requires careful optimization by professional researchers to achieve a good fusion effect. Therefore, it is necessary to evaluate the investment in terms of resources and time when choosing this method.

For the multi-sensor data fusion problem of small sample data, the migration learning method can be considered to migrate the trained model to the target task to complete the training and fusion tasks of a small amount of data. The following is a small-sample multi-sensor data fusion method based on transfer learning:
(1) Pre-training multi-modal feature extractor: First, use large-scale data to pre-train a multi-modal feature extractor, such as using depth Convolutional neural network (DCNN) or multi-channel autoencoder (MCAE) to extract shared features of multiple sensor data and minimize model overfitting.
(2) Fine-tuning for the target task: cut off the middle layer of the trained multi-modal feature extractor as the input of the target task, and then further fine-tune the output layer of the network for small sample data, so as to realize the fusion of multi-sensor data .
(3) Solve the problem of imbalance between sensors: There is usually a problem of data imbalance between different sensors, so it is necessary to balance the data of different sensors, such as using methods such as oversampling or undersampling to calculate weights for balance.
(4) Processing and optimization of fusion results: The fusion results of multi-sensor data are usually multi-dimensional, so methods such as clustering or dimensionality reduction can be used to convert them into two-dimensional images or scatter diagrams for visualization and processing. Thereby better revealing the correlation between different sensors.

The main advantage of the small-sample multi-sensor data fusion method based on transfer learning is that it can complete the multi-sensor data fusion task on a very small amount of training data. At the same time, it can reduce the over-fitting problem from large-scale data and improve the generalization of the model. performance. However, this method needs to be tuned for specific tasks, and requires a large amount of computing resources as support.

Multi-sensor data fusion problems with small sample data, small amount of data, and limited computing resources

For multi-sensor data fusion problems with small sample data, small amount of data, and limited computing resources, you can consider using Bayesian theorem information fusion fault diagnosis method; fuzzy information fusion fault diagnosis method; Dempster-Shafer (DS) evidence theory information fusion fault Diagnostic methods; the main advantages of these methods are: they do not rely on expensive computing resources for large data sets, they can achieve high precision and real-time data fusion on small sample data sets, and they have high flexibility and support processing of different types and different precisions sensor data. At the same time, this method also has certain shortcomings. For example, it is necessary to establish a complex probability model for the data, and the research complexity of a large number of multi-type sensors will obviously increase.

Bayesian Theorem Information Fusion Fault Diagnosis Method

Bayesian theorem information fusion fault diagnosis method, Bayesian theorem information fusion fault diagnosis method is a reliability analysis method based on Bayesian theorem. The method improves the accuracy and reliability of fault diagnosis by fusing fault information from different sources and using Bayesian theorem for probabilistic inference. The specific steps of the method are as follows:
(1) Collect fault information from different sources, such as sensor data, historical fault data, operator reports, and so on.
(2) Establish a fault model and carry out probability modeling for different fault types. For each fault type, a probability distribution function of its occurrence is established.
(3) Calculate the posterior probability of a certain fault type by using Bayesian theorem. Based on the prior probability and the collected information from different sources, the posterior probability of the fault type is calculated using Bayesian theorem.
(4) Fusion of posterior probabilities of different fault types. For each fault type, the posterior probability is calculated, and the posterior probabilities of different fault types are fused to obtain the final fault diagnosis result.

The Bayesian theorem information fusion fault diagnosis method has high accuracy and reliability, and can be applied to various fault diagnosis scenarios, such as mechanical equipment faults, power system faults, etc.

Fuzzy Information Fusion Fault Diagnosis Method

The fuzzy information fusion fault diagnosis method is a fault diagnosis method based on fuzzy logic. Its main idea is to fuse multi-source uncertainty information through fuzzy reasoning, so as to improve the accuracy and reliability of fault diagnosis. The specific steps of the method are as follows:
(1) Collecting fault information of various sensors and instruments. Due to the uncertainty and ambiguity of the fault information given by data sources such as sensors and instruments, it is necessary to transform these information into fuzzy information.
(2) Normalize the fuzzy information so that it occupies the same weight under the same degree of membership. This helps to improve the accuracy of the information fusion process.
(3) Use fuzzy comprehensive evaluation method to fuse fuzzy information. There are many fuzzy comprehensive evaluation methods, such as fuzzy weighted average method, fuzzy analytic hierarchy process and so on.
(4) Perform fuzzy reasoning and fuzzy matching on the fused information to obtain fault diagnosis results. Fuzzy reasoning is based on fuzzy logic and can consider many different factors, including sensor data, harsh environmental influences, historical faults, etc., thereby improving the accuracy and reliability of diagnostic results.

The fuzzy information fusion fault diagnosis method has high adaptability and reliability, and can be applied to various fault diagnosis scenarios of systems and equipment. At the same time, this method can also be extended to the case of multi-source information fusion, thereby improving the effect and reliability of fault diagnosis.

Dempster-Shafer (DS) Evidence Theory Information Fusion Fault Diagnosis Method

Dempster-Shafer (DS) evidence theory information fusion fault diagnosis method is one of the more popular fault diagnosis methods at present. This method is based on the combination of Bayesian theory and evidence theory, and has good advantages in information fusion. The specific steps of the method are as follows:
(1) Collecting fault information of various sensors and instruments. These information have different weights and confidence levels in different scenarios, so weight assignment and uncertainty description are required.
(2) Construct evidence collection. For each fault diagnosis problem, a corresponding evidence set needs to be constructed according to different evidence sources.
(3) Use DS evidence theory for information fusion. DS evidence theory can reasonably and effectively integrate different evidences, improving the precision and accuracy of diagnostic results.
(4) Use Bayesian theorem for fault diagnosis. The information obtained through the fusion of DS evidence theory is further calculated and reasoned by using Bayesian theorem to obtain the result of fault diagnosis.

DS evidence theory information fusion fault diagnosis method has good credibility and reliability. It can make full use of multi-source information, solve the problem of information uncertainty, and ensure the correctness of fault diagnosis results. The method is applicable to various fault diagnosis scenarios of systems and devices.

In the existing data fusion technology, Dempster-Shafer (DS) evidence theory shows its superiority because of its fusion algorithm is simple, practical, concise and clear, and has been widely used in data fusion and pattern recognition. Since the theory came out in the 1970s, it has gradually become a hot topic of research, and has been widely used in some military and electronic technology fields, and has also been applied in power plants and power systems. But the DS evidence theory also has its inherent defects, which leads to its bottleneck in application. Therefore, solving the application bottleneck of evidence theory has become the direction of research in this field.