Functional Principal Component Analysis of Vibrational Signal Data: A Functional Data Analytics Approach for Fault Detection and Diagnosis of Internal Combustion Engines

Fault detection and diagnosis is a critical component of operations management systems. The goal of FDD is to identify the occurrence and causes of abnormal events. While many approaches are available, data-driven approaches for FDD have proven to be robust and reliable. Exploiting these advantages, the present study applied functional principal component analysis (FPCA) to carry out feature extraction for fault detection in internal combustion engines. Furthermore, a feature subset that explained 95% of the variance of the original vibrational sensor signal was used in a multilayer perceptron to carry out prediction for fault diagnosis. Of the engine states studied in the present work, the ending diagnostic performance shows the proposed approach achieved an overall prediction accuracy of 99.72 %. These results are encouraging because they show the feasibility for applying FPCA for feature extraction which has not been discussed previously within the literature relating to fault detection and diagnosis

DATA-DRIVEN TECHNIQUES FOR DIAGNOSING BEARING DEFECTS IN INDUCTION MOTORS

Induction motors are frequently used in many automated systems as a major driving force, and thus, their reliable performances are of predominant concerns. Induction motors are subject to different types of faults and an early detection of faults can reduce maintenance costs and prevent unscheduled downtime. Motor faults are generally related to three components: the stator, the rotor and/or the bearings. This study focuses on the fault diagnosis of the bearings, which is the major reason for failures in induction motors. Data-driven fault diagnosis systems usually include a classification model which is supported by an efficient pre-processing unit. Various classifiers, which aim to diagnose multiple bearing defects (i.e., ball, inner race and outer race defects of different diameters), require well-processed data. The pre-processing tasks plays a vital role for extracting informative features from the vibration signal, reducing the dimensionality of the features and selecting the best features from the feature pool. Once the vibration signal is perfectly analyzed and a proper feature subset is created, then fault classifiers can be trained. However, classification task can be difficult if the training dataset is not balanced. Induction motors usually operate under healthy condition (than faulty situation), thus the monitored vibration samples relate to the normal state of the system expected to be more than the samples of the faulty state. Here, in this work, this challenge is also considered so that the classification model needs to deal with class imbalance problem

Efficient feature extraction of vibration signals for diagnosing bearing defects in induction motors

This paper presents a model to extract and select a proper set of features for diagnosing bearing defects in induction motors. An efficient pre-processing of the vibration signals is of paramount importance to provide informative features for the fault classification module. The vibration signals are firstly analyzed by the wavelet packet transform to extract informative frequency domain features. The dimension of the set of extracted features is reduced by resorting to linear discriminant analysis to provide a small-size set of informative features for decision making. The fault classification module contains different classifiers that can learn the features-faults relations and classify multiple bearing defects including ball, inner race and outer race defects of different diameters. Experimental results verify the effectiveness of the proposed technique for diagnosing multiple bearing defects in induction motors