Boosted Convolutional Neural Network Algorithm for the Classification of the Bearing Fault form 1-D Raw Sensor Data

Abstract

Renewable energy sources are a growing branch of industry. One such source is wind farms, which have significantly increased their number over recent years. Alongside the increased number of turbines, maintenance problems are growing. There is a need for newer and less intrusive predictive maintenance methods. About 40% of all turbine failures are due to bearing failure. This paper presents a modified neural direct classifier method using raw accelerometer measurements as input. This proprietary platform allows for better damage prediction results than convolutional networks in vibration spectrum image analysis. It operates in real time and without signal processing methods converting the signal to a time–frequency spectrogram. Image processing methods can extract features from a set of preset features and based on their importance. The proposed method is not based on feature extraction from image data but on automatically finding a set of features from raw tabular data. This fact significantly reduces the computational cost of detection and improves the failure detection accuracy compared to the classical methods. The model achieved a precision of 99.32% on the validation set, and 96.3% during bench testing. These results were an improvement over the method that classifies time–frequency spectrograms of 97.76% for the validation set and 90.8% for the real-world tests, respectively.

An Improved Spectral Subtraction Method for Eliminating Additive Noise in Condition Monitoring System Using Fiber Bragg Grating Sensors

“…In the CM system, acoustic emission (AE) or vibration signals that can reflect the fault characteristics of rotating machinery are measured by various sensors (e.g., AE sensors [ 5 ], piezoelectric sensors [ 6 ], and fiber optic sensors [ 7 ]). Due to the advantages of flexibility, corrosion resistance, immunity to electromagnetic interference, small size, and light weight, the fiber optic sensors, which can be divided into three categories including point, quasidistributed, and distributed sensors, have attracted more attention [ 8 , 9 , 10 ].…”

Wind Turbine Condition Monitoring Using the SSA-Optimized Self-Attention BiLSTM Network and Changepoint Detection Algorithm

“…In contrast, with the development of acquisition, transmission, and storage technologies, data-driven-based methods have become an attractive choice in the WTCM field, which only expands on the measured data instead of accurate physical or mathematical knowledge. Recently, numerous data-driven-based methods have been proposed in the literature and widely employed for WTCM methods, including vibration signal analysis [13][14][15][16], oil signal analysis [17], acoustic emission signal monitoring [18,19], electrical signal analysis [20], and others. However, the above-mentioned methods require the installation of additional signal acquisition equipment, which would result in a substantial improvement in the investment cost [21].…”