Papers by Dr. Himanshu Borade
Energies
The accurate prediction of the remaining useful life (RUL) of Li-ion batteries holds significant ... more The accurate prediction of the remaining useful life (RUL) of Li-ion batteries holds significant importance in the field of predictive maintenance, as it ensures the reliability and long-term viability of these batteries. In this study, we undertake a comprehensive analysis and comparison of three distinct machine learning models—XDFM, A-LSTM, and GBM—with the objective of assessing their predictive capabilities for RUL estimation. The performance evaluation of these models involves the utilization of root-mean-square error and mean absolute error metrics, which are derived after the training and testing stages of the models. Additionally, we employ the Shapley-based Explainable AI technique to identify and select the most relevant features for the prediction task. Among the evaluated models, XDFM consistently demonstrates superior performance, consistently achieving the lowest RMSE and MAE values across different operational cycles and feature selections. However, it is worth notin...
Sensors
Tool wear is an important concern in the manufacturing sector that leads to quality loss, lower p... more Tool wear is an important concern in the manufacturing sector that leads to quality loss, lower productivity, and increased downtime. In recent years, there has been a rise in the popularity of implementing TCM systems using various signal processing methods and machine learning algorithms. In the present paper, the authors propose a TCM system that incorporates the Walsh–Hadamard transform for signal processing, DCGAN aims to circumvent the issue of the availability of limited experimental dataset, and the exploration of three machine learning models: support vector regression, gradient boosting regression, and recurrent neural network for tool wear prediction. The mean absolute error, mean square error and root mean square error are used to assess the prediction errors from three machine learning models. To identify these relevant features, three metaheuristic optimization feature selection algorithms, Dragonfly, Harris hawk, and Genetic algorithms, were explored, and prediction r...
Batteries
Accurate lithium-ion battery state of health evaluation is crucial for correctly operating and ma... more Accurate lithium-ion battery state of health evaluation is crucial for correctly operating and managing battery-based energy storage systems. Experimental determination is problematic in these applications since standard functioning is necessary. Machine learning techniques enable accurate and effective data-driven predictions in such situations. In the present paper, an optimized explainable artificial intelligence (Ex-AI) model is proposed to predict the discharge capacity of the battery. In the initial stage, three deep learning (DL) models, stacked long short-term memory networks (stacked LSTMs), gated recurrent unit (GRU) networks, and stacked recurrent neural networks (SRNNs) were developed based on the training of six input features. Ex-AI was applied to identify the relevant features and further optimize Ex-AI operating parameters, and the jellyfish metaheuristic optimization technique was considered. The results reveal that discharge capacity was better predicted when the j...
Fault detection and diagnosis of gear transmission systems have attracted considerable attention ... more Fault detection and diagnosis of gear transmission systems have attracted considerable attention in recent years, due to the need to decrease the downtime on production machinery and to reduce the extent of the secondary damage caused by failures. This paper deals with fault diagnosis of a spur gearbox having spalling defect in driver gear using narrow band demodulation technique through MATLAB software. For this an experimental setup is fabricated. The vibration signals are captured from the experiments and the burst in the vibration signal is focused in the analysis and the frequency of the faulty gear is found out.
Paripex Indian Journal of Research, Jul 1, 2013
Fault detection and diagnosis of gear transmission ystems have attracted considerable attention i... more Fault detection and diagnosis of gear transmission ystems have attracted considerable attention in re cent years, due to the need to decrease the downtime on product i n machinery and to reduce the extent of the secon dary damage caused by failures. This paper deals with fault dia gnosis of a spur gearbox having spalling defect in dr ver gear using narrow band demodulation technique through MATLAB s oftware. For this an experimental setup is fabricat ed. The vibration signals are captured from the experiments a d the burst in the vibration signal is focused i n the analysis and the frequency of the faulty gear is found out.
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Papers by Dr. Himanshu Borade