Integrating Physics and Data Driven Cyber-Physical System for Condition Monitoring of Critical Transmission Components in Smart Production Line

Abstract

In response to the lack of a unified cyber–physical system framework, which combined the Internet of Things, industrial big data, and deep learning algorithms for the condition monitoring of critical transmission components in a smart production line. In this study, based on the conceptualization of the layers, a novel five-layer cyber–physical systems framework for smart production lines is proposed. This architecture integrates physics and is data-driven. The smart connection layer collects and transmits data, the physical equation modeling layer converts low-value raw data into high-value feature information via signal processing, the machine learning modeling layer realizes condition prediction through a deep learning algorithm, and scientific decision-making and predictive maintenance are completed through a cognition layer and a configuration layer. Case studies on three critical transmission components—spindles, bearings, and gears—are carried out to validate the effectiveness of the proposed framework and hybrid model for condition monitoring. The prediction results of the three datasets show that the system is successful in distinguishing condition, while the short time Fourier transform signal processing and deep residual network deep learning algorithm is superior to that of other models. The proposed framework and approach are scalable and generalizable and lay the foundation for the extension of the model.

Data mining in predictive maintenance systems: A taxonomy and systematic review

“…This work also compares its proposal with both traditional ML (kNN, SVM), and DL models (Deep Belief Network [DBN], Stacked Auto-Encoder [SAE] and simple CNN). Another framework focused on failures detection from vibration data is found in (Song et al, 2021). Several DL architectures are compared achieving the best performance with the CNN-based ResNet.…”

“…In this way, the aim is to simulate behaviors that could occur in a real system to which there is not access or which has not yet failures that can be used to train the models. In this line, there are many works that collect vibration signals from different experimental rotors (Cakir et al, 2021; Li et al, 2019; Liang et al, 2020; Liao et al, 2016; Pang et al, 2020; Qian et al, 2019; Satishkumar & Sugumaran, 2017; Song et al, 2021; Una et al, 2017; Wang et al, 2018; Wang, Zhang, et al, 2020; Yang, Lei, et al, 2019; Zhang, Li, Wang, et al, 2019), fans (Sampaio et al, 2019; Xu et al, 2021; Zenisek, Holzinger, & Affenzeller, 2019), centrifugal pumps (Hu et al, 2020), air compressors (Cupek et al, 2018) or industrial robots (Panicucci et al, 2020). Some works (Venkataswamy et al, 2020; Zenisek, Holzinger, & Affenzeller, 2019; Zenisek, Kronberger, et al, 2019) generate synthetic data from a mathematical approach that models the behavior of their problem, using for that specialized software in simulation.…”

“…Specifically, several works use the Fast Fourier Transformation (FFT) in order to reduce the feature space (Aqueveque et al, 2021; Casoli et al, 2019; Sampaio et al, 2019; Shamayleh et al, 2020; Zschech et al, 2019). The study of Song et al (2021) employed the Short‐time Fourier Transform, which focuses on determining the sinusoidal frequency and phase content of local sections of the signal as it changes over time. Another analysis uses the Wavelet Transform which is based on applying operations that affect the time, but not the shape, so that the transformed signal contains both frequency and time information.…”

“…. The study ofSong et al (2021) employed the Short-time Fourier Transform, which focuses on determining the sinusoidal frequency and phase content of local sections of the signal as it changes over time. Another analysis uses the Wavelet Transform which is based on applying operations that affect the time, but not the shape, so that the transformed signal contains both frequency and time information.…”

Remaining Useful Life Prediction of Rolling Bearings Based on Multi-Scale Attention Residual Network

“…New industrial paradigms, including network collaborative manufacturing, industrial big data, and cyber-physical systems, have promoted the transformation of traditional manufacturing to intelligent manufacturing [1]. High accuracy, high reliability, and high efficiency are the main development directions of rotary machinery health monitoring against the background of intelligent manufacturing [2].…”