Fatigue Crack Length Estimation and Prediction using Trans-fitting with Support Vector Regression

A method is described in this paper for crack propagation prediction using only the initial crack length of the target specimen. The proposed method consists of two parts: (1) crack length estimation using support vector regression (SVR) and (2) crack length prediction using a new trans-fitting method. Features based on the filtered wave signals were defined and a model was constructed using the SVR method to estimate the crack length. The hyper-parameters of the SVR model were selected based on a grid search algorithm. Prediction of the crack length was based on the previous crack length, which was estimated based on the wave signals. In this step, a newly proposed trans-fitting method was applied. The proposed trans-fitting method updated the selected candidate function to translocate the trend of crack propagation based on the training dataset. By translocating the trends to the estimated crack length of the target specimen, the crack propagation could be predicted. The proposed method was validated by comparison with given specimens. The results show that the proposed method can estimate and predict the crack length accurately

Fault Detection of PMSM under Non-Stationary Conditions Based on Wavelet Transformation Combined with Distance Approach

This paper proposes a new method to detect mechanical faults of permanent magnet synchronous motors (PMSMs) under variable speed conditions. Several prior studies have proposed motor current signature analysis (MCSA) based methods for transient conditions; however, these methods have limitations because they require the characteristic frequency of the motor or they only verify the performance of the methods for a restricted time-varying region. Thus, the research outlined in this paper suggests a method for detecting motor faults using stator currents. The proposed method uses two techniques, continuous wavelet transform (CWT) and distance approach. In this method, after the influence of the non-stationary condition is reduced in the wavelet coefficients, the distance of the residual signal from the distribution of normal state is calculated. The performance of the proposed method is confirmed with the simulation result examining unbalance. From the results, the proposed method demonstrates better performance in small-load under non-stationary conditions.N

Inter-turn Short Circuit Fault Detection in Permanent Magnet Synchronous Motors Based on Reference Voltage

This paper proposes a method for detecting inter-turn short circuit faults of surface-mounted permanent magnet synchronous motors (SPMSMs) based on reference voltage. The proposed method consists of three steps. First, from the mathematical model of faulty PMSM, a reference voltage vector in the dq-frame is separated into a healthy voltage and a faulty voltage. Second, the faulty voltage is further decomposed into a DC component and a 2nd harmonic component. Third, the relationship between these components and the inter-turn fault severity is identified. Based on the relationship, this paper proposes two fault indicators; DC bias and 2nd harmonic amplitude. Simulation results verify the effectiveness of the proposed method. The proposed method can detect inter-turn short circuit faults under low sampling rate and without the need for additional sensors. This strength can facilitate on-line fault detection.N

Drive-Tolerant Current Residual Variance (DTCRV) for Fault Detection of a Permanent Magnet Synchronous Motor Under Operational Speed and Load Torque Conditions

This paper proposes a novel method that uses stator current signals to detect motor faults under operational speed and load torque conditions. Previous studies on motor current signature analysis (MCSA) have been devoted to developing methods to detect faults in non-stationary conditions; however, they have limitations. Conventional methods require much domain knowledge or parameter selection for signal decomposition, and are applicable under limited variable conditions. Thus, this paper proposes a new feature, drive-tolerant current residual variance (DTCRV), for fault detection. This new approach requires no domain knowledge and is applicable under varying speed and load torque conditions. In the proposed method, first, the envelope of the current signal is calculated to extract its modulation. Second, the drive-related signal, which greatly varies based on speed and load torque conditions, is extracted from the enveloped current signal. Third, the drive-tolerant current residual (DTCR) is calculated; the DTCR is defined as the subtraction of the drive-related signal from the enveloped current signal. Finally, the new health feature is calculated as the variance of the DTCR. To demonstrate the proposed method, experimental studies were conducted under several operating conditions (i.e., different speed profiles and load torque levels) with two fault modes: 1) a stator inter-turn short and 2) misalignment. Results confirm the ability of DTCRV to promptly and accurately detect faults in a variety of conditions; in contrast, conventional methods are greatly affected by the operating conditions.Y