An improved convolutional neural network with an adaptable learning rate towards multi-signal fault diagnosis of hydraulic piston pump

Permutation entropy-based improved uniform phase empirical mode decomposition for mechanical fault diagnosis

Condition monitoring and fault diagnosis of wind turbines is an attractive yet challenging task. This paper presents a novel data-driven fault diagnosis scheme for wind turbines, using refined time-shift multiscale fluctuation-based dispersion entropy (RTSMFDE) and cosine pairwise-constrained supervised manifold mapping (CPCSMM). The developed RTSMFDE can improve multiscale dispersion entropy in complexity measurement of time series, by employing the refined time-shift analysis approach. Moreover, the proposed CPCSMM combines cosine distance metric, supervised theory and sparse global manifold structure, to obtain the most important features, resulting in a low dimensional representation. In this fault diagnosis scheme, a high-dimensional fault feature set is first constructed using RTSMFDE. Then, sensitive features are extracted using CPCSMM. Finally, the low-dimensional feature set is fed to a beetle antennae search-based support vector machine for fault identification. Simulation experiments and wind turbine fault diagnosis experiments were used, to show that the proposed RTSMFDE can comprehensively mine the wind turbine features and outperforms the existing state-of-the-art entropy methods. The visualization results and classification effects of the proposed CPCSMM approach are better than existing unsupervised and supervised dimensionality reduction methods. The proposed data-driven fault diagnosis method can effectively and accurately identify multi-faults and outperforms existing fault diagnosis techniques.

Data-driven fault diagnosis for wind turbines using modified multiscale fluctuation dispersion entropy and cosine pairwise-constrained supervised manifold mapping

Timely and effective fault detection is essential to ensure the safe and reliable operation of wind turbines. However, due to the complex kinematic mechanisms and harsh working environments of wind turbine equipment, it is difficult to extract sensitive features and detect faults from acquired wind turbine signals. To address this challenge, a novel intelligent fault detection scheme for constant-speed wind turbines based on refined time-shifted multiscale fuzzy entropy (RTSMFE), supervised isometric mapping (SI), and adaptive chaotic Aquila optimization-based support vector machine (ACAOSVM) is proposed. In the first step, the RTSMFE method is used to fully extract features of the wind turbine system. The time-shifted coarse-grained construction technique and a refined computing technique are adopted in the RTSMFE method to enhance the capability of traditional multiscale fuzzy entropy for measuring the complexity of signals. Subsequently, an effective manifold learning approach, SI, is applied to obtain the important and low-dimensional feature set from the high-dimensional feature set. Finally, sensitive features are fed into the ACAOSVM classifier to identify faults. The proposed ACAO algorithm is used to optimize important parameters of the SVM, thereby improving its detection performance. Simulations and wind turbine experiments verified that the proposed RTSMFE outperforms existing entropy techniques in terms of complexity measurement and feature extraction. Furthermore, the proposed ACAOSVM classifier is superior to existing advanced classifiers for fault pattern recognition. Finally, the proposed intelligent fault detection scheme can more correctly and efficiently detect wind turbine single/hybrid faults than other recently published schemes. 

Intelligent fault detection scheme for constant-speed wind turbines based on improved multiscale fuzzy entropy and adaptive chaotic Aquila optimization-based support vector machine.

Multi-Sensor data fusion in intelligent fault diagnosis of rotating machines: A comprehensive review

Fault diagnosis of rolling bearing using marine predators algorithm-based support vector machine and topology learning and out-of-sample embedding

Rolling bearing is a key component of rotating machines, its working state directly affects the performance and safety of the whole equipment. Deep learning based on big data is a mainstream means of intelligent mechanical fault diagnosis. The key lies in enhancing fault feature and improving diagnosis accuracy. Different from the Convolution Neural Network (CNN) which relies on the convolution layer to extract the image features, the Vision Transformer (VIT) uses the multi-head attention mechanism to establish the relationship among the pixels in an image. In order to improve the accuracy of rolling bearing fault diagnosis, a new fault diagnosis method based on VIT is proposed. The vibration gray texture images to be input are divided into the patches according to the predetermined size and linearly mapped into input sequences, and the global image information is integrated through the self-attention mechanism to realize fault diagnosis. In order to enhance the expressiveness and generalization ability, the pooling layer is introduced into VIT. The tested results show that the fault diagnosis accuracy of VIT on the test set reaches 94.6%, and the corresponding classification indexes top-1 is 84.2% and top-5 is 95.0%. The accuracy of the new Pooling Vision Transformer (PIT) is 3.3% higher than that of the original VIT, which proves that the introduction to pooling layer can improve the image identification performance of VIT.

New intelligent fault diagnosis approach of rolling bearing based on improved vibration gray texture image and vision transformer

In order to accurately realize the contact fatigue state identification of specimen, a new method based on vibration and image heterogeneous data, as well as on D-S evidence theory, is proposed. Firstly, combined with the bearing public data set from CWRU, the vibration signal imaging methods such as SDP, GAF and GRI, as well as neural network models such as VGG16, ResNet and S-T, were compared and analyzed. It is determined that the SDP method is used to visualize the vibration signal, and the two state identification evidence bodies based on the vibration information source are obtained through the VGG16 and ResNet models. Secondly, combined with image monitoring signals, the fatigue defect identification method based on automatic weighting threshold and the detection error dynamic compensation method based on fatigue defect edge features are used to quantify the fatigue damage area and obtain the state identification evidence body based on the image information source. On this basis, a state identification network model based on vibration and image spatiotemporal heterogeneous data is constructed, and the D-S evidence theory is used to realize the contact fatigue state identification of the specimen. The results show that fusion of vibration and image data can achieve information complementarity and may identify the contact fatigue state of the specimen more accurately. The accuracy of state identification after fusion is 98.67%, which is at least 3% higher than that of a single information source. This research is of great significance for the accurate acquisition of material contact fatigue properties and has certain reference value for the heterogeneous data fusion from different sources.

/pdf/contact-fatigue-state-identification-of-specimen-based-on-199mnfc4.pdf

Contact Fatigue State Identification of Specimen Based on Heterogeneous Data and Evidence Theory

In order to deal with the shortcomings (such as poor robustness) of the traditional single-channel vibration signal in the comprehensive monitoring of the gearbox fault state, a multi-channel decision-level fusion algorithm was proposed based on symmetrized dot pattern (SDP) analysis, with the visual geometry group 16 network (VGG16) fault diagnosis model. Firstly, the SDP method was used to convert the vibration signal of a single multi-channel sensor into an imaging arm. Secondly, the obtained image arm was input into the VGG16 convolutional neural network in order to train the fault diagnosis model that can be obtained. Then, the SDP images of the signals that were to be measured from multiple multi-channel sensors were input into the fault diagnosis model, and the diagnosis results of multiple multi-channel sensors could then be obtained. Experimentally, it was demonstrated that the diagnostic results of multi-channel sensors one, two, and three were more accurate than those of single-channel sensors one, two, and three, by 3.01%, 16.7%, and 5.17%, respectively. However, the fault generation was not generated in a single direction, but rather multiple directions. In order to improve the comprehensiveness of the raw vibration data, a fusion method using DS (Dempster–Shafer) evidence theory was proposed in order to fuse multiple multi-channel sensors, in which the accuracy achieved 99.93% when sensor one and sensor two were fused, which was an improvement of 8.88% and 1.02% over single sensors one and two, respectively. When sensor one and sensor three were fused, the accuracy reached 99.31%, which was an improvement of 8.31% and 6.17% over single sensors one and three, respectively. When sensor two and sensor three were fused, the accuracy reached 99.91%, which was an improvement of 1.00% and 6.74% over single sensors two and three, respectively. When three sensors were fused simultaneously, the accuracy reached 99.99%, which was 8.93%, 1.08%, and 6.81% better than single sensors one, two, and three, respectively. Therefore, it can be proved that the number of sensor channels has a great influence on the diagnosis results.

/pdf/gearbox-fault-diagnosis-based-on-multi-sensor-and-multi-1e345tp1.pdf

Gearbox Fault Diagnosis Based on Multi-Sensor and Multi-Channel Decision-Level Fusion Based on SDP

To address the problem of the low recognition rate of time-frequency domain methods gearbox fault identification, a method featuring decision-level fusion of DS evidence theory and GA-BP algorithm was proposed in the present study. Firstly, the fault data of each state of the gearbox was classified, based on which the time-frequency domain features were extracted and 19 significant features have been selected. Secondly, the accuracy of the traditional BP algorithm was compared with that of the GA-BP algorithm. On this basis, it has been concluded that the GA-BP algorithm is highly accurate, and the local diagnostic results obtained by the GA-BP algorithm have been used as the basic probability. Finally, the DS evidence theory is currently used to fuses with the GA. In addition, the final fault identification of the gearbox can be achieved by using the DS evidence theory and the multi-sensor local diagnosis results obtained by the GA-BP algorithm for decision fusion. The results of the simulations and experiments showed that the method proposed has improved accuracy over a single algorithm for fault identification of gearboxes, respectively.

https://www.mdpi.com/2076-3417/12/6/3106/pdf?version=1647593916

Multi-Sensor GA-BP Algorithm Based Gearbox Fault Diagnosis

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