Online Predictive Maintenance Monitoring Adopting Convolutional Neural Networks

Abstract

Thermal, electrical and mechanical stresses age the electrical insulation systems of high voltage (HV) apparatuses until the breakdown. The monitoring of the partial discharges (PDs) effectively assesses the insulation condition. PDs are both the symptoms and the causes of insulation aging and—in the long term—can lead to a breakdown, with a burdensome economic loss. This paper proposes the convolutional neural networks (CNNs) to investigate and analyze the aging process of enameled wires, thus predicting the life status of the insulation systems. The CNNs training does not require any kind of assumption of how the factors (e.g., voltage, frequency and temperature) contribute to the life model. The experiments confirm that the proposal obtains better estimations of the life status of twisted pair specimens concerning existing solutions, which are based on strong hypotheses about the life model dependency on the factors.

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

“…The approaches have presented magnificent contributions to PdM tasks . Deep neural networks (DNNs) covering CNNs, RNNs, and LSTMs have been used to generate the proposed predictive maintenance strategies and algorithms and improve their prediction accuracy [53][54][55][56][57][58][59][60][61][62][63][64]. In these works, the networks have been applied to extract the significant features from raw sensor data, including images or the data in the form of time series, and to detect, recognize, or predict sudden and expected changes in the system according to the features.…”

An Exploration of the Reform of English Informatisation Teaching in Colleges and Universities Based on Deep Learning Model and Microteaching Mode

“…Neural networks can fit various highly complex functions well due to the activation function [19]- [21]. The design of activation functions in neural networks stems from the exploration of the brain's biological structure, where each neuron in the brain is not equally active for information.…”

Interpretation of Eccentricity of an Enameled Wire by Capacitance Measurements

“…In the event of poor centering, the insulation will have thinner thicknesses in certain places. These will be vulnerable areas as the imposed electrical field is higher, which will lead to the premature appearance of partial discharges accelerating the aging of the insulation and leading to a premature fault [11][12][13][14][15]. Therefore, the enameled wire concentricity is a parameter that intervenes in the lifespan of the windings of the electric machines.…”