TY - JOUR
T1 - A data-based technique for monitoring of wound rotor induction machines: A simulation study
AU - Harrou, Fouzi
AU - Ramahaleomiarantsoa, Jacques F.
AU - Nounou, Mohamed N.
AU - Nounou, Hazem N.
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2016/5/9
Y1 - 2016/5/9
N2 - Detecting faults induction machines is crucial for a safe operation of these machines. The aim of this paper is to present a statistical fault detection methodology for the detection of faults in three-phase wound rotor induction machines (WRIM). The proposed fault detection approach is based on the use of principal components analysis (PCA). However, conventional PCA-based detection indices, such as the T2T2 and the Q statistics, are not well suited to detect small faults because these indices only use information from the most recent available samples. Detection of small faults is one of the most crucial and challenging tasks in the area of fault detection and diagnosis. In this paper, a new statistical system monitoring strategy is proposed for detecting changes resulting from small shifts in several variables associated with WRIM. The proposed approach combines modeling using PCA modeling with the exponentially weighted moving average (EWMA) control scheme. In the proposed approach, EWMA control scheme is applied on the ignored principal components to detect the presence of faults. The performance of the proposed method is compared with those of the traditional PCA-based fault detection indices. The simulation results clearly show the effectiveness of the proposed method over the conventional ones, especially in the presence of faults with small magnitudes.
AB - Detecting faults induction machines is crucial for a safe operation of these machines. The aim of this paper is to present a statistical fault detection methodology for the detection of faults in three-phase wound rotor induction machines (WRIM). The proposed fault detection approach is based on the use of principal components analysis (PCA). However, conventional PCA-based detection indices, such as the T2T2 and the Q statistics, are not well suited to detect small faults because these indices only use information from the most recent available samples. Detection of small faults is one of the most crucial and challenging tasks in the area of fault detection and diagnosis. In this paper, a new statistical system monitoring strategy is proposed for detecting changes resulting from small shifts in several variables associated with WRIM. The proposed approach combines modeling using PCA modeling with the exponentially weighted moving average (EWMA) control scheme. In the proposed approach, EWMA control scheme is applied on the ignored principal components to detect the presence of faults. The performance of the proposed method is compared with those of the traditional PCA-based fault detection indices. The simulation results clearly show the effectiveness of the proposed method over the conventional ones, especially in the presence of faults with small magnitudes.
UR - http://hdl.handle.net/10754/609006
UR - http://linkinghub.elsevier.com/retrieve/pii/S2215098615302020
UR - http://www.scopus.com/inward/record.url?scp=85017377927&partnerID=8YFLogxK
U2 - 10.1016/j.jestch.2016.04.008
DO - 10.1016/j.jestch.2016.04.008
M3 - Article
SN - 2215-0986
VL - 19
SP - 1424
EP - 1435
JO - Engineering Science and Technology, an International Journal
JF - Engineering Science and Technology, an International Journal
IS - 3
ER -