Deep model integrated with data correlation analysis for multiple intermittent faults diagnosis

This paper presents a learning system with a K-nearest neighbour classifier to classify the wear condition of a multi-piston positive displacement pump. The first part reviews current built diagnostic methods and describes typical failures of multi-piston positive displacement pumps and their causes. Next is a description of a diagnostic experiment conducted to acquire a matrix of vibration signals from selected locations in the pump body. The measured signals were subjected to time-frequency analysis. The signal features calculated in the time and frequency domain were grouped in a table according to the wear condition of the pump. The next step was to create classification models of a pump wear condition and assess their accuracy. The selected model, which best met the set criteria for accuracy assessment, was verified with new measurement data. The article ends with a summary.

Section: Introductionmentioning

confidence: 99%

Use of the K-Nearest Neighbour Classifier in Wear Condition Classification of a Positive Displacement Pump

Konieczny

Stojek

2021

“…Even so, WT still suffers some drawbacks when processing large amounts of nonlinear vibration signals [ 6 ]. Empirical Mode Decomposition (EMD) was successfully applied to the fault diagnosis of the vibration signal; however, after extracting impulsive features from the vibration signals, the mode mixing problem remained [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory

Gao

Kim

2021

Deep learning (DL) plays a very important role in the fault diagnosis of rotating machinery. To enhance the self-learning capacity and improve the intelligent diagnosis accuracy of DL for rotating machinery, a novel hybrid deep learning method (NHDLM) based on Extended Deep Convolutional Neural Networks with Wide First-layer Kernels (EWDCNN) and long short-term memory (LSTM) is proposed for complex environments. First, the EWDCNN method is presented by extending the convolution layer of WDCNN, which can further improve automatic feature extraction. The LSTM then changes the geometric architecture of the EWDCNN to produce a novel hybrid method (NHDLM), which further improves the performance for feature classification. Compared with CNN, WDCNN, and EWDCNN, the proposed NHDLM method has the greatest performance and identification accuracy for the fault diagnosis of rotating machinery.

“…To be more specific, the fault diagnosis methods based on feature extraction, feature selection, and feature fusion have been studied in [ 9 , 10 , 11 , 12 , 13 ], and the accuracies of fault diagnosis are improved greatly by these methods. The intermittent fault detection, isolation, and diagnosis of train multi-axis speed sensors are addressed in [ 14 , 15 , 16 ], and the composite fault diagnosis of rolling equipment such as train bearings has been proposed in [ 17 , 18 , 19 ]. These above technologies have greatly improved the level of intelligence in ensuring the safe and reliable operation of trains.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Brake Train Based on Multi-Sensor Data Fusion

Jin

Xie

et al. 2021

Self Cite

In this paper, a fault diagnosis method is proposed based on multi-sensor fusion information for a single fault and composite fault of train braking systems. Firstly, the single mass model of the train brake is established based on operating environment. Then, the pre-allocation and linear-weighted summation criterion are proposed to fuse the monitoring data. Finally, based on the improved expectation maximization, the braking modes and braking parameters are identified, and the braking faults are diagnosed in real time. The simulation results show that the braking parameters of systems can be effectively identified, and the braking faults can be diagnosed accurately based on the identification results. Even if the monitoring data are missing or abnormal, compared with the maximum fusion, the accuracies of parameter identifications and fault diagnoses can still meet the needs of the actual systems, and the effectiveness and robustness of the method can be verified.