Fault diagnosis of robot joint based on BP neural network

To improve the accuracy of centrifugal pump fault diagnosis, a novel fault diagnosis method based on improved multiple fractal detrended fluctuation analysis (MFDFA), the fusion of multi-sensing information derived from the back propagation (BP) neural network and the Dempster–Shafter (D-S) evidence theory, is accordingly proposed. Firstly, the multifractal spectral parameters of four sensor signals under four different operating conditions were extracted as centrifugal pump fault feature vectors using improved MFDFA and input to the BP neural network. Then, the basic trust assignment function was constructed by calculating trustworthiness (both local and global) as priori information, which is based on the output results of the neural networks specific to of each group of sensors. Finally, the basic trust assignment function was fused with decision processing in accordance with the D-S evidence combination rule in order to effectively achieve the multi-sensor information fusion centrifugal pump fault diagnosis. The experimental results show the multiple fractal spectrum feature parameters extracted by the improved MFDFA can accurately reflect the signal essence, and can be used as the fault feature vector. On this basis, this multi-sensor fault diagnosis reduces the uncertainty of fault classification and demonstrates improved accuracy compared to the single-sensor fault diagnosis thanks to being based on a combination of the BP neural networks and D-S evidence theory. Thereby, this method can facilitate accurate diagnosis of the centrifugal pump fault type with high confidence, subsequently providing a novel and alternative method to existing methods of diagnosis.

The problem of the wind turbine gearbox fault diagnosis was investigated by employing the support vector machine (SVM), which is optimized by the improved fruit fly intelligent algorithm with decreasing steps. First of all, the fault characteristic value extracted by Hilbert transform envelope is presented. Secondly, the general SVM solution to the wind turbine gearbox fault diagnosis problem is presented, where the improved fruit fly intelligent algorithm is adopted to optimize the performance of the model of the fault detection and diagnosis. Thirdly, the effectiveness of three fault diagnosis models are compared, including the fault detection and diagnosis model by the traditional SVM, its improved model optimized by the partical swarm optimization algorithm, and improved model by the SVM optimized by the proposed approach. By using the proposed optimization algorithm, the accuracy of gearbox fault diagnosis is much better than other two models, which are validated by the extensive simulation results based on practical historical operation data.

Many assessment and detection methods are used to diagnose faults in machines. High accuracy in fault detection and diagnosis can be achieved by using numerical methods with noise-resistant properties. However, to some extent, noise always exists in measured data on real machines, which affects the identification results, especially in the diagnosis of early- stage faults. In view of this situation, a damage assessment method based on blind source separation and dynamic fuzzy neural network (DFNN) is presented to diagnose the early-stage machinery faults in this paper. In the processing of measurement signals, blind source separation is adopted to reduce noise. Then sensitive features of these faults are obtained by extracting low dimensional manifold characteristics from the signals. The model for fault diagnosis is established based on DFNN. Furthermore, on-line computation is accelerated by means of compressed sensing. Numerical vibration signals of ball screw fault modes are processed on the model for mechanical fault diagnosis and the results are in good agreement with the actual condition even at the early stage of fault development. This detection method is very useful in practice and feasible for early-stage fault diagnosis.

Rolling bearings are vital components in rotary machines where even minor faults can lead to the collapse of the entire mechanical system, resulting in significant economic losses. The condition monitoring and fault diagnosis of rolling bearings are crucial for safe operation of complex industrial systems. Traditional fault diagnosis methods mainly depend on expert experience, which leads to obvious deficiencies in diagnostic efficiency, especially in terms of accuracy. To address the issues of traditional bearing fault diagnosis relying on expert experience and low efficiency and accuracy of fault diagnosis, a bearing fault diagnosis model combining synchrosqueezed wavelet transform (SWT) and SE-ResNet is proposed. Firstly, the one-dimensional non-stationary bearing vibration signal is converted into a high-frequency two-dimensional time-frequency image through SWT, which serves as the input for the convolutional neural network; Secondly, an attention mechanism is introduced to construct an SE-ResNet model for extracting two-dimensional image features, which enhances the attention of important features and thus enhances the network's representation ability; Finally, the results of fault diagnosis are output through Softmax layer. To validate the capacity of the proposed model, experiments with the bearing dataset collected from Case Western Reserve University (CWRU) were conducted. The results show that the transfer learning accuracy of the CWRU dataset is 86.25% to 99%. This method has good feasibility, good convergence performance, and has high practical application value.

In order to realize a stable and effective gearbox fault diagnosis system, an uncertain information fusion method based on BP neural network and modified DS evidence theory is proposed. The common gearbox fault diagnosis system consists of two dominating parts: signal feature extraction and fusion decision. Actually, the main task of most papers is to extract the characteristics of gearbox fault with various algorithms, whereas the fusion decision part is still absent. To manage the integration of gearbox fault diagnosis in the fusion decision part, this paper puts forward a novel fusion method by the introduction of two-layer fusion. In this paper, BP neural network is used to establish the local-layer fusion, while an improved DS evidence theory is taken to build decision-layer fusion. It can be seen from the definition and derivation of the two-layer fusion, the original method settles the uncertainty of multi-sensor information fusion in gearbox fault diagnosis system. According to the experiment results and analyses, the innovative algorithm improves the accuracy of fault identification and diagnosis, in addition, reduces the uncertainty of fault diagnosis system.

To improve the reliability of power grid fault diagnosis by enhancing the processing ability of uncertain information and adequately utilizing the alarm information about power grids, a fault diagnosis method using intuitionistic fuzzy Petri Nets based on time series matching is proposed in this paper. First, the alarm hypothesis sequence and the real alarm sequence are constructed using the alarm information and the general grid protection configuration model, and the similarity of the two sequences is used to calculate the timing confidence. Then, an intuitionistic fuzzy Petri Nets fault diagnosis model, with an excellent ability to process uncertain information from intuitionistic fuzzy sets, is constructed, and the initial place value of the model is corrected by the timing confidence. Finally, an application of the fault diagnosis model for the actual grid is established to analyze and verify the diagnostic results of the new method. The results for some test cases show that the new method can improve the accuracy and fault tolerance of fault diagnosis, and, furthermore, the abnormal state of the component can be inferred.

To improve the reliability and accuracy of a transformer fault diagnosis model based on a backpropagation (BP) neural network, this study proposed an enhanced distributed parallel firefly algorithm based on the Taguchi method (EDPFA). First, a distributed parallel firefly algorithm (DPFA) was implemented and then the Taguchi method was used to enhance the original communication strategies in the DPFA. Second, to verify the performance of the EDPFA, this study compared the EDPFA with the firefly algorithm (FA) and DPFA under the test suite of Congress on Evolutionary Computation 2013 (CEC2013). Finally, the proposed EDPFA was applied to a transformer fault diagnosis model by training the initial parameters of the BP neural network. The experimental results showed that: (1) The Taguchi method effectively enhanced the performance of EDPFA. Compared with FA and DPFA, the proposed EDPFA had a faster convergence speed and better solution quality. (2) The proposed EDPFA improved the accuracy of transformer fault diagnosis based on the BP neural network (up to 11.11%).

DGA (Dissolved Gas Analysis) is the traditional transformer fault diagnosis method, but it mainly depends on the experience of operators. In order to solve the limitations of traditional method, this paper introduces intelligent method for fault diagnosis of transformer. The intelligent method made fusion of various data, including SCADA data, oil dissolved gas sensor data, related electrical test data, operation maintenance records, and so on, employed space-time weighting fusion method based on BP neural network, and put forward the model of transformer fault diagnosis based on multi-source information fusion, which improved the accuracy of the transformer fault diagnosis dramatically.

Satellite attitude control system (SACS) is prone to faults due to its complexity and harsh working environment. If the fault is not detected in a timely manner to take appropriate countermeasures, it may lead to severe consequences. Traditional fault diagnosis methods have certain limitations, such as the need for accurate mathematical models or enough experience of fault diagnosis. Therefore, this paper proposes a fault diagnosis method for SACS sensors that integrates feature extraction with an optimized back propagation (BP) neural network. Firstly, the features of simulation data are extracted by wavelet packet energy (WPE), which can effectively highlight the fault characteristics. Then the osprey optimization algorithm (OOA) is used to optimize the initial weights and biases of BP neural network, so as to solve the problem that BP neural network is easy to fall into local minimum. The simulation results show that the proposed method can accurately detect and identify various fault types of sensors in SACS, thus providing a new and effective way for fault diagnosis of SACS.

In this paper, for the failure of spraying workshop door switch, exhaust fan failure and other faults in the spraying workshop, a fault diagnosis algorithm based on the BP (Back Propagation) neural network is proposed to solve the fault diagnosis problem in the spraying workshop. First, the sensor network of the spraying workshop was built. Multi-source sensors were used to realize the dynamic perception of the on-site environment and equipment status. Through the preprocessing of multi-source heterogeneous data, a unified data template and interface were established. Secondly, the BP neural network algorithm is used to simulate and diagnose the possible faults in the spraying process. Finally, MATLAB test simulation proves its high accuracy of fault diagnosis.

Main bearing that plays the role of supporting and making the cutter to rotate and tunnel is the core part of TBM. Because of the harsh working conditions and complex changeful construction, the axial and radial load and environmental factors such as temperature of TBM main bearing are changing to make the fault of main bearing presenting randomness, and then the not easy identified fault may be produced. The traditional neural network model can not dynamic consider the cause of the reasons, parts and types, BP neural network fault diagnosis model based on fault reasons—signs matrix is presented in this paper. Firstly, the faults are screened through the fault reasons—symptom matrix, and the neural network structure is designed according to the screening result, then, the fault type is identified by way of model training. According to TBM main bearing fault symptoms data provided by a heavy enterprises practical engineering and MATLAB simulation validation, the feasibility and superiority of this model method are proved.

According to the complex fault mechanism of direct current (DC) charging points for electric vehicles (EVs) and the poor application effect of traditional fault diagnosis methods, a new kind of fault diagnosis method for DC charging points for EVs based on deep belief network (DBN) is proposed, which combines the advantages of DBN in feature extraction and processing nonlinear data. This method utilizes the actual measurement data of the charging points to realize the unsupervised feature extraction and parameter fine-tuning of the network, and builds the deep network model to complete the accurate fault diagnosis of the charging points. The effectiveness of this method is examined by comparing with the backpropagation neural network, radial basis function neural network, support vector machine, and convolutional neural network in terms of accuracy and model convergence time. The experimental results prove that the proposed method has a higher fault diagnosis accuracy than the above fault diagnosis methods.

This paper proposes a new method for fault diagnosis of new energy transmission system based on combined BiLSTM-1DCNN model. The EV dataset is preprocessed and converted into a format acceptable to the model. The BiLSTM neural network is used to extract temporal features, and the 1DCNN network is used to capture the spatial features of the data. The combination of the two networks can effectively improve the accuracy and efficiency of fault diagnosis. Comparing the proposed method with traditional machine learning methods, it is found that the proposed method has higher accuracy and faster convergence speed. Sensitivity analysis is used to study the impact of different hyperparameters on model performance. The results show that the performance of the model is affected by the number of layers, learning rate and window size in the 1DCNN network. In addition, the experiments and datasets of this study are from ADVISOR of the US Department of Energy (DOE). The proposed combined BiLSTM-1DCNN model provides a promising method for fault diagnosis of new energy drive systems, which can improve the efficiency and accuracy of fault diagnosis.

In an intelligent manufacturing context, the smooth operations of mechanical equipment in the production process of enterprises and timely fault diagnosis during operations have become increasingly important. However, the effect of traditional fault diagnosis depends on the feature extraction quality and experts’ empirical knowledge, which is inefficient and costly, and cannot match the needs of mechanical equipment fault diagnosis in intelligent manufacturing. The TSK fuzzy system has a strong approximation capability and the ability to interpret expert knowledge. The broad learning system (BLS) has strong feature extraction and fast computation capabilities. In this paper, we present a new model—the TSK fuzzy broad learning system (TSK-BLS). The model integrates the advantages of the BLS and the fuzzy system at the same time, which can be calculated quickly and accurately by pseudo-inverse and symmetry methods. On the other hand, the model is an embedded model-building mechanism, which extends the application scope of BLS theory. The model was tested on a bearing fault data set, provided by Case Western Reserve University, and the model’s fault diagnosis accuracy was as high as 0.9967. The results were compared with those of the convolutional neural network (CNN) and the BLS models, whose fault diagnosis accuracies are 0.6833 and 0.9133, respectively. Comparison showed that the proposed fault diagnosis model—TSK-BLS, achieved significant improvements.

Comparative study on deep transfer learning strategies for cross-system and cross-operation-condition building energy systems fault diagnosis