The shaft and bearing are the most critical components in rotating machinery. Majority of problems arise from faulty bearings in turn affect the shaft. The vibration signals are widely used to determine the condition of machine elements. The vibration signals are used to extract the features to identify the status of a machine. This paper presents the use of c-SVC and nu-SVC models of support vector machine (SVM) with four kernel functions for classification of faults using statistical features extracted from vibration signals under good and faulty conditions of rotational mechanical system. Decision tree algorithm was used to select the prominent features. These features were given as inputs for training and testing the c-SVC and nu-SVC model of SVM and their fault classification accuracies were compared.

Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine

In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

Fault Detection and Diagnosis of Induction Motors Using Motor Current Signature Analysis and a Hybrid FMM–CART Model

In this paper, a review on condition monitoring of induction motors is first presented Then, an ensemble of hybrid intelligent models that is useful for condition monitoring of induction motors is proposed The review covers two parts, ie, (i) a total of nine commonly used condition monitoring methods of induction motors; and (ii) intelligent learning models for condition monitoring of induction motors subject to single and multiple input signals Based on the review findings, the Motor Current Signature Analysis (MCSA) method is selected for this study owing to its online, non-invasive properties and its requirement of only single input source; therefore leading to a cost-effective condition monitoring method A hybrid intelligent model that consists of the Fuzzy Min–Max (FMM) neural network and the Random Forest (RF) model comprising an ensemble of Classification and Regression Trees is developed The majority voting scheme is used to combine the predictions produced by the resulting FMM–RF ensemble (or FMM–RFE) members A benchmark problem is first deployed to evaluate the usefulness of the FMM–RFE model Then, the model is applied to condition monitoring of induction motors using a set of real data samples Specifically, the stator current signals of induction motors are obtained using the MCSA method The signals are processed to produce a set of harmonic-based features for classification using the FMM–RFE model The experimental results show good performances in both noise-free and noisy environments More importantly, a set of explanatory rules in the form of a decision tree can be extracted from the FMM–RFE model to justify its predictions The outcomes ascertain the effectiveness of the proposed FMM–RFE model in undertaking condition monitoring tasks, especially for induction motors, under different environments

Condition monitoring of induction motors: A review and an application of an ensemble of hybrid intelligent models

A step-by-step fuzzy diagnostic method based on frequency-domain symptom extraction and trivalent logic fuzzy diagnosis theory (TLFD), which is established by combining the trivalent logic inference theory with the possibility and fuzzy theories, is proposed herein. The features for diagnosing a number of abnormal states are extracted sequentially from the measured signals using statistical tests in the frequency domain. The symptom parameters (SPs) that can sensitively reflect symptoms of abnormal states are then selected to provide effective information for the discrimination of each state. The membership function of each state is then generated based on the possibility theory using the probability functions of the SPs. The step-by-step fuzzy diagnoses are performed based on the TLFD. This method can be used extensively to diagnose anomalies in various equipment. In this study, the diagnosis of structure faults of a rotating machine is cited as an example to demonstrate the effectiveness and universality of this method.

Step-by-Step Fuzzy Diagnosis Method for Equipment Based on Symptom Extraction and Trivalent Logic Fuzzy Diagnosis Theory

http://app.eng.ubu.ac.th/~app/resproject/upload/p1/2.paper_l_suparerk.2552.pdf

Bearing condition diagnosis and prognosis using applied nonlinear dynamical analysis of machine vibration signal

Evaluation of gas turbine rotor dynamic analysis using the finite element method

Experimental analysis on fault detection for a direct coupled rotor-bearing system

A neural network predictor is designed for analyzing vibration parameters of the rotating system. The vibration parameters (amplitude, velocity, acceleration in vertical direction) are measured at the bearing points. The system's vibration and noise are analyzed with and without load. The designed neural predictor has three (input, hidden, output) layers. In the hidden layer, 10 neurons are used for approximation. The results show that the network is useful as an analyzer of such systems in experimental applications. The neural networks are validated for reduced test data with unknown faults.

Design of artificial neural networks for rotor dynamics analysis of rotating machine systems

In spite of advanced electro-mechanical technology on passenger or load elevators, elevator accidents still occur. Therefore, it is necessary to analyze vibrations of elevators with and without load for predicting some possible faults on their mechanical parts. This study proposes an adaptive neural network predictor to estimate and evaluate the vibrations on elevator systems. For this purpose, elevator vibrations are measured from two points of the elevator system for different working conditions, and different types of neural network analyzers are employed to evaluate the system vibrations. Simulation results show that neural networks can be used as an adaptive analyzer for such systems in the experimental applications.

The Use of Neural Network Predictors for Analyzing the Elevator Vibrations

Purpose – To improve the application neural networks predictors on bearing systems and to investigate the exact neural model of the ball‐bearing system.Design/methodology/approach – A feed forward neural network is designed to model‐bearing system. Two results are compared for finding the exact model of the system.Findings – The results of the proposed neural network predictor gives superior performance for analysing the behaviour of ball bearing undergoing loading deformation.Research limitations/implications – The results of the proposed neural network exactly follows desired results of the system. Neural network predictor can be employed in practical applications.Practical implications – As theoretical and practical study is evaluated together, it is hoped that ball‐bearing designers and researchers will obtain significant results in this area.Originality/value – This paper fulfils an identified research results need and offers practical investigation for an academic career and research. Also, It shoul...

Hamdi Taplak

Papers

Evaluation of gas turbine rotor dynamic analysis using the finite element method

Experimental analysis on fault detection for a direct coupled rotor-bearing system

Design of artificial neural networks for rotor dynamics analysis of rotating machine systems

The Use of Neural Network Predictors for Analyzing the Elevator Vibrations

An artificial neural network application to fault detection of a rotor bearing system