- T249, Techno Building, Kumoh National Institute of Technology 61 Daehak-ro, Gumi, Gyeongbuk, 39177, Korea
- +821066501046
Tanvir Alam Shifat
Oregon State University, Electrical and Computer Engineering, Graduate Student
- Kumoh National University of Technology, Mechanical System engineering, Graduate Studentadd
Multiple sensor data fusion is necessary for effective condition monitoring as the electric machines operate in a wide range of diverse operations. This study investigates sensor acquired vibration and current signals to establish a... more
Multiple sensor data fusion is necessary for effective condition monitoring as the electric machines operate in a wide range of diverse operations. This study investigates sensor acquired vibration and current signals to establish a reliable multi-fault diagnosis framework of a brushless DC (BLDC) motor. Faults in stator and rotor were created deliberately by shorting two adjacent windings and creating a hole on the surface, respectively. The threshold for different health states was obtained by the third harmonic analysis of motor current. Later, the key features from sensor acquired current and vibration signals are selected based on monotonicity and reduced using the principal component analysis (PCA). For future predictions, an artificial neural network (ANN) is used to classify different fault features and its performance is evaluated using several metrics. Analysis of motor current harmonics and impulsive vibration response at the same time provides a thorough health estimation of BLDC motor in the presence of both electrical and mechanical faults. Multiple sensor information is fused to obtain a better understanding of the fault characteristics and mitigate the randomness of fault diagnosis. The proposed model was able to detect and classify multiple fault features with higher accuracy compared to other similar methods.
Research Interests:
Brushless DC motor, also referred to as BLDC motor, has been a widely used electric machine due to its excellent performance over conventional DC motors. Due to complex operating conditions and overloading, several irregularities can take... more
Brushless DC motor, also referred to as BLDC motor, has been a widely used electric machine due to its excellent performance over conventional DC motors. Due to complex operating conditions and overloading, several irregularities can take place in a motor. Stator related faults are among the most commonly occurring faults in BLDC motor. With an initial raise in local heating, a fault in the stator can largely reduce motor efficiency and account for the entire system breakdown. In this study, we present a deep learning-based approach to estimate the remaining useful life (RUL) of BLDC motor affected by different stator related faults. To analyze the motor health degradation, we have investigated two types of stator faults namely inter-turn fault (ITF) and winding short-circuit fault (WSC). A generator was coupled with the motor and using an average value rectifier (AVR), generator's output voltage was monitored for the entire lifecycle. A proven neural network for effective sequence modeling, recurrent neural network (RNN) is selected to train the voltage degradation data. For a better estimation of nonlinear trends, long-short term memory (LSTM) with attention mechanism is chosen to make predictions of the motor RUL for both types of faults. The main concern that encourages authors of this paper is the proposed method can be used for the real-time condition monitoring and health state estimation of BLDC motors. Also, the proposed AVR-LSTM method is not affected by environmental influences, making it suitable for diverse operating conditions. INDEX TERMS Attention mechanism, BLDC motor, remaining useful life, LSTM, stator fault.
Research Interests:
The reliability and performance of a system with minimum life-cycle cost have become quite prominent in engineering systems. With increasing industrial applications, machines are operating in intricate conditions with higher uncertainty,... more
The reliability and performance of a system with minimum life-cycle cost have become quite prominent in engineering systems. With increasing industrial applications, machines are operating in intricate conditions with higher uncertainty, causing greater vulnerability of system failure. This paper reports fault-related information of Brushless DC Motor (BLDC motor) in non-stationary operating conditions and presents several analyses to diagnose the faults. Fault diagnosis is the most crucial and important part of system prognostics which helps to increase the remaining useful life (RUL) and prevent catastrophic failures. Having both electrical and mechanical characteristics present in a BLDC motor, it shows several faults in different operating conditions. These faults cause a significant change in the vibration of the Motor. This paper deals with the anomaly detection of BLDC motor in non-stationary speed conditions using vibration signal analysis as well as extraction of several Condition Indicators (CI).
Research Interests:
Brushless DC (BLDC) motors, bearing the characteristics of permanent magnet synchronous machines, have gained immense popularity in industrial applications due to its excellent efficiency and ease in control over conventional DC motors.... more
Brushless DC (BLDC) motors, bearing the characteristics of permanent magnet synchronous machines, have gained immense popularity in industrial applications due to its excellent efficiency and ease in control over conventional DC motors. Stator related faults are the most common types of faults in BLDC motors while operating under a higher loading and complex condition. Conventional machine learning (ML) classifiers such as-Support Vector Machines (SVM), k-nearest neighbors (KNN), Naïve Bayes (NB) classifiers fail to obtain optimum accuracy when there are abrupt changes in health states of the BLDC motor. Especially the classification of weak health features ends up with an erroneous result. Boosting techniques allow adding weights to weak features and a better result in classification. This study presents an improved stator related fault classification of BLDC Motor using AdaBoost technique. Several parameters of AdaBoost algorithm are optimized and implemented along with Random Forest (RF) classifier in order to determine a developed algorithm for fault classification.
Research Interests:
Electric motor is a prominent rotary machinery in many engineering applications due to its excellent electrical energy utilization. With the increased demand in production and complex operating conditions, motors often run in a severe... more
Electric motor is a prominent rotary machinery in many engineering applications due to its excellent electrical energy utilization. With the increased demand in production and complex operating conditions, motors often run in a severe loading condition. Overload, overheating and many other intricate operating conditions account for the stator related faults in motors. Motor current signature analysis (MCSA) and vibration analysis have been popular techniques to diagnose different stator and rotor related faults in motors. But it is difficult to find the fault magnitude or fault threshold by using only one approach due to nonstationary motor operations. This paper presents a comprehensive review of a permanent magnet brushless DC motor's (BLDC motor) fault diagnosis combining vibration and current signals collected from sensors. Since the insulation break in the stator winding is the most commonly occurring fault in the stator, a short-circuit was artificially created between two windings. Based on the motor operating conditions, three health states are chosen from the experimental sensor data with different fault magnitudes. Health states are labeled as healthy state, incipient failure state, and severe failure state. Two effective fault diagnosis indices named kurtosis and third harmonic of motor current are selected for analyzing the vibration signals and current signals, respectively. Proposed diagnostics framework is validated using experimental data and proven to detect the stator fault at the early stage as well as distinguish between different fault states. Monitoring both mechanical and electrical characteristics of BLDC motor provides a thorough understanding of fault magnitude and threshold in different health states.