Condition monitoring

About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.

Mode Identification of Hybrid Systems in the Presence of Fault

Abstract: A mode identification method for hybrid system diagnosis is proposed The method is presented as a module of a quantitative health monitoring framework for hybrid systems After fault occurrence, the fault is detected and isolated The next step is fault parameters estimation, where the size of the fault is identified Fault parameter estimation is based on data collected from the hybrid system while the system is faulty, and its dynamical model is partially unknown A hybrid system's dynamics consists of continuous behavior and discrete states represented by modes Fault parameter estimation requires knowledge of the monitored system's operating mode The new method utilizes the partially known dynamical model to identify hybrid system modes in the presence of a single parametric fault

Wireless health monitoring system for vibration detection of induction motors

Abstract: To avoid unexpected equipment failures and obtain higher accuracy in diagnostic for the predictive maintenance of induction motors, on-line health monitoring system plays an important role to improve the system reliability and availability. Among different techniques of fault detection, work on motor current signature analysis by using only stator current spectra has been well documented. In addition, the recent developments in MEMS technology shows increasing trend in integrating vibration analysis for fault diagnostic. Vibration-based detection by using the accelerometer is gaining popularity due to high reliability, low power consumption, and low cost. This paper presents the study of vibration due to the rotor imbalance. The technique of vibration detection and observation of vibration signal in the 3-phase induction machine is studied. A novel health monitoring system of electric machine based on wireless sensor network (ZigBee™/IEEE802.15.4 Standard) is proposed and developed in this paper. Experimental results of the proposed severity detection technique of rotor vibration under different levels of imbalance conditions are investigated and discussed.

Bearing Fault Classification of Induction Motors Using Discrete Wavelet Transform and Ensemble Machine Learning Algorithms

Abstract: Bearing fault diagnosis at early stage is very significant to ensure seamless operation of induction motors in industrial environment. The identification and classification of faults helps to undertook maintenance operation in an efficient manner. This paper presents an ensemble machine learning-based fault classification scheme for induction motors (IMs) utilizing the motor current signal that uses the discrete wavelet transform (DWT) for feature extraction. Three wavelets (db4, sym4, and Haar) are used to decompose the current signal, and several features are extracted from the decomposed coefficients. In the pre-processing stage, notch filtering is used to remove the line frequency component to improve classification performance. Finally, the two ensemble machine learning (ML) classifiers random forest (RF) and extreme gradient boosting (XGBoost) are trained and tested using the extracted feature set to classify the bearing fault condition. Both classifier models demonstrate very promising results in terms of accuracy and other accepted performance indicators. Our proposed method achieves an accuracy slightly greater than 99%, which is better than other models examined for the same dataset.