Faulty gear diagnosis using weighted PCA with swish activated BLSTM classifier

Abstract

The early faulty gear diagnosis is most necessary in the industry. In the current decade, with the tremendous growth of ANN (Artificial Neural Network), the researcher planned to use DL (Deep Learning) methods to sketch out faults in gear in an early stage. Traditional gear fault diagnosis method mostly utilizes deep NN (Neural Network) related to tine sequence of gathered signals. In this instance, feature extraction in the direction of inverse time domain signal is commonly ignored. To overcome this issue, here in this paper, proposed Weighted Principal Component Analysis (WPCA) and BLSTM (Bi-Directional Long Short Term Memory) along with Swish Activation function for faulty gear diagnosis from the vibration signals. WPCA is utilized to extract multi-scale features related to faulty gear from the vibration signal. Likewise, BLSTM is used to classify the extracted features to diagnose the fault in an earlier stage. Several experiments were conducted to evaluate the proposed work of categorizing the defects in gear from the vibrating signal. Experiments were conducted on three kinds of the dataset to classify the type of faulty gear accurately. The proposed work proves its superiority in organizing the gear faults in a most efficient way than existing methods.

Using Optimized Long Short-Term Memory For Time-Series Forecasting of Electric Vehicles Battery Charging

“…Real-world datasets from Jeju Island, South Korea, served as their testbed, wherein their proposed model was deemed superior in aggregating fast-charging power demand. [13] brought forth a mixed LSTM neural network, which, unlike traditional LSTMs, segmented various feature types and processed them distinctly within its mixed neural network architecture. Benchmarked against numerous innovative Machine Learning (ML) and DL models using the EV charging data from the city of Dundee, UK, this method exhibited exceptional predictive accuracy.…”