Discrete Cosine Transformation and Temporal Adjacent Convolutional Neural Network-Based Remaining Useful Life Estimation of Bearings

Abstract

In recent years, several time-frequency representation (TFR) and convolutional neural network- (CNN-) based approaches have been proposed to provide reliable remaining useful life (RUL) estimation for bearings. However, existing methods cannot tackle the spatiotemporal continuity between adjacent TFRs since temporal proposals are considered individually and their temporal dependencies are neglected. In allusion to this problem, a novel prognostic approach based on discrete cosine transformation (DCT) and temporal adjacent convolutional neural network (TACNN) is proposed. Wavelet transform (WT) is applied to effectively map the raw signals to the time frequency domain. Considering the high load and complexity of model computation, bilinear interpolation and DCT algorithm are introduced to convert TFRs into low-dimensional DCT spectrum coding matrix with strong sparsity. Furthermore, the TACNN model is proposed which is capable of learning discriminative features for temporal adjacent DCT spectrum coding matrix. Effectiveness of the proposed method is verified on the PRONOSTIA dataset, and experiment results show that the proposed model is able to realize automatic high-precision estimation of bearings RUL with high efficiency.

Deep Learning-Enhanced Small-Sample Bearing Fault Analysis Using Q-Transform and HOG Image Features in a GRU-XAI Framework

“…When applied to a noisy signal condition, DCT may effectively emphasize specific fault-induced frequencies, which helps with accurate problem identification and classification. DCT's compact energy description feature makes it possible to identify anomalies, wear, or failure mechanisms in equipment by extracting significant features from raw diagnostic data and analyzing them effectively [26]. As a result, using DCT in fault diagnosis procedures improves the precision and dependability of problem detection, enabling early warning systems and preventative maintenance procedures to reduce expensive downtime and equipment failures.…”

Design of a system for diagnosing the technical condition of a gas pumping unit based on the analysis of existing methods of processing vibroacoustic signals

“…У роботі [5] наведено аналіз відеосигналу за допомогою швидкого перетворення Фур'є і вирахувано частоту миготіння вихрового пальника, що потім доведено емпірично. У роботі [6] за допомогою швидкого перетворення Фур'є було визначено частоти та амплітуди вібраційних сигналів від пневматично спарених турбін (одна -компресор, другавільного ходу) із сигналу від усієї системи, однак подальший аналіз був неможливим, оскільки по номограмі Фельдмана співвідношення частот та амлітуд двох сигналів є таким, що не дозволяє чітко їх розділити, тому остаточний аналіз не було проведено, що є недоліком. За допомогою короткочасного перетворення Фур'є у роботі [7] було показано зміни при переході від робочого до неробочого стану, а саме відбулися зміни, що призвели до того, що найбільшою амплітудою стала володіти не перша гармоніка, а п'ята.…”

Research on Prediction Method of Gear Pump Remaining Useful Life Based on DCAE and Bi-LSTM

“…As the machinery industry is also moving towards the era of big data, deep learning is being widely used in fault diagnosis and life prediction of rotating machinery devices [25]. Many researchers have improved common deep learning models, such as CNN, long shortterm memory (LSTM), deep belief network (DBN) and generative adversarial networks (GANs), and have demonstrated good effectiveness in pattern recognition of rotating machinery [26][27][28][29][30][31]. The research results show that deep learning has broad application prospects in the field of mechanical equipment PHM.…”