Prognostics and health management (PHM) is a framework that offers comprehensive yet individualized solutions for managing system health. In recent years, PHM has emerged as an essential approach for achieving competitive advantages in the global market by improving reliability, maintainability, safety, and affordability. Concepts and components in PHM have been developed separately in many areas such as mechanical engineering, electrical engineering, and statistical science, under varied names. In this paper, we provide a concise review of mainstream methods in major aspects of the PHM framework, including the updated research from both statistical science and engineering, with a focus on data-driven approaches. Real world examples have been provided to illustrate the implementation of PHM in practice.

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Prognostics and Health Management: A Review on Data Driven Approaches

Gear crack level identification based on weighted K nearest neighbor classification algorithm

This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. In order to evaluate the proposed algorithm, the data sets obtained from vibration signals and current signals of the induction motors are used. The results indicate that the CART-ANFIS model has potential for fault diagnosis of induction motors.

http://eprints.hud.ac.uk/16575/1/(2009%2DESWA)_Fault_diagnosis_using_ANFIS.pdf

Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networks

Fault detection prior to their occurrence or complete shut-down in induction motor is essential for the industries. The fault detection based on condition monitoring techniques and application of machine learning have tremendous potential. The power of machine learning can be harnessed and optimally used for fault detection. The faults especially in induction motor needs to be addressed at a proper time for avoiding losses. Machine learning algorithm applications in the domain of fault detection provides a reliable and effective solution for preventive maintenance. This paper presents a review of the machine learning algorithm applications in fault detection in induction motors. This paper also presents the future prospects and challenges for an efficient machine learning based fault detection systems.

Review on Machine Learning Algorithm Based Fault Detection in Induction Motors

The use of features selection and nearest neighbors rule for faults diagnostic in induction motors

This paper presents the application of pattern recognition methods in order to detect broken bars and stator unbalance in an induction motor Some time or frequency dependent parameters, which are relevant for fault detection, are described They are used to build up a pattern vector Then two decision methods are proposed The first one is based on the k-nearest neighbors (kNN) rule The second one based on linear discriminant functions determination The principles of two decision rules are introduced and the diagnosis results obtained are compared

Fault diagnosis in an induction motor by pattern recognition methods

The necessity to insure a continuous and safety operation for induction motors, involves preventive maintenance programs with fault detection techniques. This paper presents the application of a pattern recognition approach in order to detect broken bars in an induction motor. The aim is to identify the operating conditions according to the level of load. For this purpose, only electrical measurements are used. Some time or frequency-dependent parameters, which are relevant for fault detection, are described. They are used to build up a pattern vector. This vector is represented by a point and the operating condition of the induction motor are represented by classes in the space. As the dimension of the space is greater than three, the classes and their evolutions can be visualized after a principal component analysis. Then a decision system, based on this signature and the k-nearest neighbors rule, is proposed.

Broken bars detection in an induction motor by pattern recognition

Intensive research efforts have been focused on the signature analysis (SA) to detect electrical and mechanical fault condition of induction machines Different signals can be used: voltage, current, flux and power This paper is a comparative analysis of two different diagnosis methods that are used to detect and localize failures in induction motors The first one is based on leakage flux measurement and the second one is a diagnosis method based on current, electrical power measurement and pattern recognition A synthesis of two methods will be developed It mainly concerns rotor faults

http://ieeexplore.ieee.org/iel5/9977/32035/01490275.pdf

Comparative study of two diagnosis methods induction machine

Induction motors present numerous advantages due to their robustness and their power-weight ratio. Thus, they are widely used in the industry. The necessity to insure continuous and safety operation involves preventive maintenance programs. This paper presents the application of a pattern recognition approach to detect electrical faults in an induction motor. For this purpose, only electrical measurements are used. The origin and the causes of faults are presented, with their distribution on the machine components. Some time or frequency-dependent parameters, relevant for fault detection are described. They are used to build up a pattern vector. Then a decision system, based on this signature and the k-nearest neighbours is proposed.

http://ieeexplore.ieee.org/iel5/8325/26406/01176319.pdf

Roland Casimir

Papers

The use of features selection and nearest neighbors rule for faults diagnostic in induction motors

Fault diagnosis in an induction motor by pattern recognition methods

Broken bars detection in an induction motor by pattern recognition

Comparative study of two diagnosis methods induction machine

A decision system to detect failures in induction motors