A remaining useful life prediction method based on PSR-former

Abstract

The non-linear and non-stationary vibration data generated by rotating machines can be used to analyze various fault conditions for predicting the remaining useful life(RUL). It offers great help to make prognostic and health management(PHM) develop. However, the complexity of the mechanical working environment makes the vibration data collected easily affected, so it is hard to form an appropriate health index(HI) to predict the RUL. In this paper, a PSR-former model is proposed including a Phase space reconstruction(PSR) layer and a Transformer layer. The PSR layer is utilized as an embedding to deepen the understanding of vibration data after feature fusion. In the Transformer layer, an attention mechanism is adopted to give different assignments, and a layer-hopping connection is used to accelerate the convergence and make the structure more stable. The effectiveness of the proposed method is validated through the Intelligent Maintenance Systems (IMS) bearing dataset. Through analysis, the prediction accuracy is judged by the parameter RMSE which is 1.0311. Some state-of-art methods such as LSTM, GRU, and CNN were also analyzed on the same dataset to compare. The result indicates that the proposed method can effectively establish a precise model for RUL predictions.

A Novel Customised Load Adaptive Framework for Induction Motor Fault Classification Utilizing MFPT Bearing Dataset

“…It can effectively cancel noise for bearings diagnosis when the optimal order is chosen [31]. Each technique has strengths and weaknesses; the periodogram lacks precision, Welch's method struggles with rapid shifts, and the multitaper method is computationally intense [6,32]. The AR model's model with an order of 𝑝, represented as AR(P), can be expressed in Equation 1 [29]:…”

“…One of the most famous techniques is wavelet transform, which offers the unique advantage of localising this frequency information in time, exemplified by the term "timescale (frequency) based analysis method." The wavelet transform predominantly employs three mother wavelet functions: Amor, Bump, and Morse, which have become staple choices in signal pre-processing [32]. The Wavelet scalogram based on CWT is a foundational tool in wavelet-based fault diagnostics [6].…”

“…Another pivotal technique is WSE, which merges the wavelet transform, singular value decomposition, and Shannon entropy. WSE adeptly extracts fault characteristics even from noiseladen and subtle fault transients, cementing its efficacy and superiority in fault detection [32,33].…”

“…The CWT produces a two-dimensional representation of the signal, with time on one axis and scale (related to frequency) on the other axis, called time-frequency domain analysis [34]. The wavelet coefficients can be used to create wavelet images for analysis and provide visual insights into how the frequency content of the signal changes over time, often referred to as a "scalogram" as in [35] or 2D time-frequency diagram" as in [32]. On the other hand, quantifying the complexity of the singular value distribution resulting from the wavelet transform is also possible.…”

“…Quantification doesn't directly involve generating images but yields a numerical measure characterising the intricate patterns and changes in the signal's frequency behaviour over time. Instead, it quantifies the singular values' distribution complexity, providing insights into the signal's features [32].…”