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.

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.

Industry 5.0, also known as the “smart factory”, is an evolution of manufacturing technology that utilizes advanced data analytics and machine learning techniques to optimize production processes. One key aspect of Industry 5.0 is using vibration data to monitor and detect anomalies in machinery and equipment. In the case of a vertical carousel storage and retrieval system (VCSRS), vibration data can be collected and analyzed to identify potential issues with the system’s operation. A correlation coefficient model was used to detect anomalies accurately in the vertical carousel system to ascertain the optimal sensor placement position. This model utilized the Fisher information matrix (FIM) and effective independence (EFI) methods to optimize the sensor placement for maximum accuracy and reliability. An LSTM-autoencoder (long short-term memory) model was used for training and testing further to enhance the accuracy of the anomaly detection process. This machine-learning technique allowed for detecting patterns and trends in the vibration data that may not have been evident using traditional methods. The combination of the correlation coefficient model and the LSTM-autoencoder resulted in an accuracy rate of 97.70% for detecting anomalies in the vertical carousel system.

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.

Due to the dynamic and complex operating conditions in industrial machinery, relying on a single sensor data can lead to an erroneous approximation of the remaining useful life (RUL). Therefore, an RUL estimation framework valid for a wide range of diverse applications requires multi-sensor diagnostics information. In this paper, we investigate sensor acquired vibration, flow rate, and pressure signals to establish a reliable fault prognostics framework of a gear pump subjected to different fuel contamination levels. A fault in the gear pump was created deliberately by adding silica (SiO2), and data were acquired for the entire lifecycle. A novel degradation index is modeled using three fault-related information: prognostibility, variance, and stochastic dominance of statistical features. The threshold for different health states was obtained by the flow and pressure data of the gear pump. Later, acquired vibration signals are analyzed for the health index (HI) construction and fused with the pressure data using an extended Kalman filter for the RUL estimation. For accurate future RUL predictions, a bi-directional long-short term memory (BiLSTM) neural network is trained and analyzed using different performance indices.