VMD-KFCM Algorithm for the Fault Diagnosis of Diesel Engine Vibration Signals

Abstract

Accurate and timely fault diagnosis for the diesel engine is crucial to guarantee it works safely and reliably, and reduces the maintenance costs. A novel diagnosis method based on variational mode decomposition (VMD) and kernel-based fuzzy c-means clustering (KFCM) is proposed in this paper. Firstly, the VMD algorithm is optimized to select the most suitable K value adaptively. Then KFCM is employed to classify the feature parameters of intrinsic mode functions (IMFs). Through the comparison of many different parameters, the singular value is selected finally because of the good classification effect. In this paper, the diesel engine fault simulation experiment was carried out to simulate various faults including valve clearance fault, fuel supply fault and common rail pressure fault. Each kind of machine fault varies in different degrees. To prove the effectiveness of VMD-KFCM, the proposed method is compared with empirical mode decomposition (EMD)-KFCM, ensemble empirical mode decomposition (EEMD)-KFCM, VMD-back propagation neural network (BPNN), and VMD-deep belief network (DBN). Results show that VMD-KFCM has advantages in accuracy, simplicity, and efficiency. Therefore, the method proposed in this paper can be used for diesel engine fault diagnosis, and has good application prospects.

A Classification Algorithm of Fault Modes-Integrated LSSVM and PSO with Parameters’ Optimization of VMD

“…According to the principle of the stationary phase, the integral of equation (10) has a maximum value at the frequency f i , which needs to meet the condition d/dt[φ(t) − 2πf i (t)] � 0, namely, f i (t) � (1/2π)(dφ(t)/dt). is conclusion indicates that the energy of nonstationary signals is mainly concentrated at the instantaneous frequency.…”

“…ese methods may alleviate the modal aliasing problem of high-frequency signals. Meanwhile, to optimally select the parameter K of VMD, the genetic variation sample group, kurtosis criterion variational mode decomposition, and self-organizing mapping (SOM) neural network have been adopted to adaptively determine the optimal value of the parameter K in [9][10][11][12]. To verify the effectiveness of these new methods, some experiment examples have been used to simulate in [13][14][15][16][17].…”

Fault Diagnosis of Rotor Broken Bar in Induction Motor Based on Successive Variational Mode Decomposition

“…Variational mode decomposition suppresses the problem of mode mixing and is beneficial to narrow band signal decomposition, but the decomposition performance of variational mode decomposition depends on the setting of parameter such as mode and penalty factor. If the setting is inaccurate, the result of extracting the fault component will become descending [24][25][26]. Successive variational mode decomposition (SVMD) which is improved from variational mode decomposition is an adaptive signal processing method.…”

Combination of Optimized Variational Mode Decomposition and Deep Transfer Learning: A Better Fault Diagnosis Approach for Diesel Engines

“…Thus, these feature extraction and dimensionality reduction techniques are combined to detect pattern recognition of various failures. Moreover, various diagnostic methods to detect faults in diesel engines have been suggested [15][16][17].…”