Wenlong Fu

Fault diagnosis based on dependent feature vector and probability neural network for rolling element bearings

Rolling element bearings (REB) are crucial mechanical parts of most rotary machineries, and REB failures often cause terrible accidents and serious economic losses. Therefore, REB fault diagnosis is very important for ensuring the safe operation of rotary machineries. In previous researches on REB fault diagnosis, achieving the accurate description of faults has always been a difficult problem, which seriously restricts the reliability and accuracy of the diagnosis results. In order to improve the precision of fault description and provide strong basis for fault diagnosis, dependent feature vector (DFV) is proposed to denote the fault symptom attributes of the six REB faults in this paper, and this is a self-adaptive fault representation method which describes each fault sample according to its own characteristics. Because of its unique feature selection technique and particular structural property, DFV is excellent in fault description, and could lay a good foundation for fault diagnosis. The advantages of DFV are theoretically proved via the Euclidean distance evaluation technique. Finally, a fault diagnosis method combining DFV and probability neural network (PNN) is proposed and applied to 708 REB fault samples. The experimental results indicate that the proposed method can achieve an efficient accuracy in REB fault diagnosis.

Design of a multi-mode intelligent model predictive control strategy for hydroelectric generating unit

This paper proposes a nonlinear multi-mode intelligent model predictive control (MPC) strategy for hydroelectric generating unit (HGU). In this multi-mode MPC scheme, excitation MPC mode and integrated MPC mode work for excitation control process and load scheduling condition, respectively. Every control mode is built on the basis of a tree-seed algorithm based model predictive control (TSA-MPC) scheme, which introduces a newly proposed tree-seed algorithm (TSA) and the stability-guaranteed measures into rolling optimization mechanism of nonlinear MPC (NMPC) to replace the existing complex numerical differential geometric solutions. Simulation experiments of the proposed multi-mode MPC and the comparative methods are undertaken under diverse operating conditions in a HGU control system as case studies. Experimental results indicate the superiority in voltage regulation and damping performance as well as the effectiveness of the comprehensive control of turbine governing and generator excitation.

A state tendency measurement for a hydro-turbine generating unit based on aggregated EEMD and SVR

The reliable measurement of state tendency for a hydro-turbine generating unit (HGU) is significant in guaranteeing the security of the unit and promoting stability of the power system. For this purpose, an aggregated ensemble empirical mode decomposition (AEEMD) and optimized support vector regression (SVR)-based hybrid model is developed in this paper in order to enhance the measuring accuracy of state tendency for a HGU. First of all, the non-stationary time series of the state signal are decomposed into a collection of intrinsic mode functions (IMFs) by EEMD. Subsequently, to obtain the refactored intrinsic mode functions (RIMFs), the IMFs with different scales are aggregated with the proposed reconstruction strategy in consideration of the frequency and energy conditions. Later, the phase–space matrix in accordance with each RIMF is deduced by phase–space reconstruction and all the RIMFs are predicted through establishing homologous optimal SVR forecasting models with a grid search. Finally, the ultimate measuring values of state tendency can be determined through the accumulation of all the RIMF forecasting values. Furthermore, the effectiveness of the proposed method is validated in engineering experiments and comparative analyses.

Fault Diagnosis for Rolling Element Bearings with VMD Time-Frequency Analysis and SVM

This paper proposes a novel fault diagnosis method for rolling element bearings based on the newly developed adaptive signal processing technique -- variational mode decomposition (VMD), combining with time-frequency feature extraction and support vector machine (SVM). For the given samples of fault signals, VMD is firstly employed to decompose the signals into collections of intrinsic mode functions (IMFs). To extract more characteristics of fault information, 20 features of each IMF are calculated from time domain and frequency domain respectively. Then fault feature vectors of all samples are established by assembling features of the IMFs belonging to the same signal. Finally, all fault feature vectors are utilized to train SVM classifier, with which the fault modes of rolling element bearings are identified. To verify the effectiveness of the proposed model, EMD is utilized for comparison during the signal decomposing stage. The experimental result shows that the proposed method has better diagnosing performance.

Identification of vibration–speed curve for hydroelectric generator unit using statistical fuzzy vector chain code and support vector machine

The faults of hydroelectric generator unit have many different symptoms, and the relation between vibration amplitude and rotating speed is an important diagnosis criterion. In this article, a novel vibration–speed curve identification approach based on statistical fuzzy vector chain code and support vector machine is proposed and applied to identify the relation between vibration amplitude and rotating speed. In the identification process, the shape features of vibration–speed curve are extracted by statistical fuzzy vector chain code at first and then input to support vector machine to identify the type of vibration–speed curve; furthermore, the fault type of hydroelectric generator unit can be determined. Compared with the previous methods, statistical fuzzy vector chain code shows the advantages of low feature dimension, simple calculation, invariance to scaling and translation, and sensitive variance to rotation. The results of identification and comparative experiments show that the proposed method is more effective and efficient and can identify the vibration–speed curve with satisfactory accuracy.

Fault diagnosis based on a novel weighted support vector data description with fuzzy adaptive threshold decision

The fault diagnosis of generator units is critical to guarantee the high efficiency of the electric system. However, detailed fault samples are difficult to obtain, and the distribution of fault samples usually shows the characteristics of unevenness and unbalance, which may lead to low fault diagnosis precision. Nevertheless, it has been seldom considered in the traditional classifier of fault diagnosis for generator units until now. In this paper, a novel fault classifier of weighted support vector data description (SVDD) with fuzzy adaptive threshold decision is proposed and applied in the fault diagnosis of generator units. To tackle the drawback that SVDD is sensitive to the distribution of samples, a novel SVDD model based on a complex weight is proposed. The complex weight is assigned with local density and size-based weight, while local density of each data point is obtained with the k-nearest neighbour approach and the size-based weight of each data point is computed according to the proportion of classes. Then the conventional SVDD is reformulated with the complex weights. Furthermore, new decision rules based on the relative distance and fuzzy adaptive threshold decision are applied to identify the class of testing samples. Finally, the proposed method is applied in the identification of several standard datasets, as well as the fault diagnosis for a turbo-generator unit. Experimental results and the engineering application reveal that the proposed method shows good performance in accuracy and universality, and is suitable for the fault diagnosis of generator units.