PHM Survey : Implementation of Signal Processing Methods for Monitoring Bearings and Gearboxes

Soualhi, Abdenour; Hawwari, Yasmine; Medjaher, Kamal; Clerc, Guy; Razik, Hubert; Guillet, François

doi:10.36001/ijphm.2018.v9i2.2736

Cited by 9 publications

(6 citation statements)

References 73 publications

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“…The use of wavelets for condition monitoring of electric motor driven mechanisms is widely reported (Peng & Chu, 2004;Yan, Gao, & Chen, 2014;Soualhi et al, 2018), especially to improve the signal-to-noise ratio or to demodulate frequency bands for diagnosis from the envelope of the spectrum.…”

Section: Estimation Of the Informative Signal With Wavelet Analysismentioning

confidence: 99%

Combining Wavelets and AR Identification for Condition Monitoring of Electric-cam Mechanisms Using PLCopen Readings of Motor Torque

Diversi,

Speciale,

Barbieri

2024

IJPHM

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This paper addresses the problem of monitoring the state of health of electric motor driven mechanisms. The proposed condition monitoring procedure belongs to the data-driven methods and employs a combination of wavelet analysis and autoregressive model identification. It exploits the fact that the torque motor signal is a readily available measurement in industrial computers complying with the PLCOpen standard and how motion controllers execute electric cams. In particular, the torque provided by the PLC is represented as the sum between the ideal torque and an additional contribution that contains information about mechanism health condition. The procedure completely removes the ideal torque and analyzes the residual component to highlight and classify possible fault conditions. The described condition monitoring procedure is tested on real data in a laboratory setup.

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Section: Estimation Of the Informative Signal With Wavelet Analysismentioning

confidence: 99%

Combining Wavelets and AR Identification for Condition Monitoring of Electric-cam Mechanisms Using PLCopen Readings of Motor Torque

Diversi,

Speciale,

Barbieri

2024

IJPHM

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“…Depending on the applied signal processing algorithms and extracted features, we can distinguish the most representative groups of methods, such as time-domain, frequencydomain and time-frequency domain. An extensive review of techniques in this category is described in articles [9,162], while here we present a brief summary.…”

Section: Signal-based Modelsmentioning

confidence: 99%

From Corrective to Predictive Maintenance—A Review of Maintenance Approaches for the Power Industry

Moleda

Małysiak-Mrozek

Ding

et al. 2023

Sensors

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Appropriate maintenance of industrial equipment keeps production systems in good health and ensures the stability of production processes. In specific production sectors, such as the electrical power industry, equipment failures are rare but may lead to high costs and substantial economic losses not only for the power plant but for consumers and the larger society. Therefore, the power production industry relies on a variety of approaches to maintenance tasks, ranging from traditional solutions and engineering know-how to smart, AI-based analytics to avoid potential downtimes. This review shows the evolution of maintenance approaches to support maintenance planning, equipment monitoring and supervision. We present older techniques traditionally used in maintenance tasks and those that rely on IT analytics to automate tasks and perform the inference process for failure detection. We analyze prognostics and health-management techniques in detail, including their requirements, advantages and limitations. The review focuses on the power-generation sector. However, some of the issues addressed are common to other industries. The article also presents concepts and solutions that utilize emerging technologies related to Industry 4.0, touching on prescriptive analysis, Big Data and the Internet of Things. The primary motivation and purpose of the article are to present the existing practices and classic methods used by engineers, as well as modern approaches drawing from Artificial Intelligence and the concept of Industry 4.0. The summary of existing practices and the state of the art in the area of predictive maintenance provides two benefits. On the one hand, it leads to improving processes by matching existing tools and methods. On the other hand, it shows researchers potential directions for further analysis and new developments.

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“…Finally, timefrequency domain features can be calculated, for instance, with the continuous wavelet transform, the discrete wavelet transform, and the wavelet packet transform (Yan, Gao, & Chen, 2014). Other popular sources of time-frequency domain features are the short time Fourier transform, the Wigner Ville distribution and the Hilbert Huang transform (Soualhi et al, 2018). The features calculated by these signal processing techniques are usually hand-picked and require domain specific knowledge.…”

Section: Review Of Previous Researchmentioning

confidence: 99%

Condition Monitoring of Slow-speed Gear Wear using a Transmission Error-based Approach with Automated Feature Selection

Sendlbeck

Fimpel

Siewerin

et al. 2021

IJPHM

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Gear flank changes caused by wear do not only affect the dynamic behavior of gear systems, but they can also compromise the load-carrying capacity of gear teeth up to critical failure. To help avoid unintended consequences like downtime or safety risks, a condition monitoring system needs to be able to estimate the current wear during operation based on available sensor measurements. While many condition monitoring approaches in research rely on vibrational analysis with manual feature engineering, gearboxes running at slow speed do not reveal much excitation information for this purpose. We therefore introduce an approach for slow-speed gear wear monitoring that is based on the dynamic gear transmission error and that contains an automated feature selection process. For this purpose, we extract a large set of features from the preprocessed transmission error samples. Applying combined filter and embedded feature selection methods enables us to automatically identify and remove features with low relevance. The selection process consists of filtering features with no statistical dependence on the target wear value, removing redundant features with a correlation analysis and a recursive feature elimination process with cross-validation based on a random forest regressor. The remaining relevant set of features is the basis for model training and subsequent wear estimation. For this, the present research employed two independent ensemble models, random forest regression and gradient boosted regression trees. To train and test the proposed approach, we conducted slow-speed gear experiments with developing gear wear on a single-stage spur gear test rig setup. The results of both models show good gear wear estimation performance compared to the actual wear mass loss, even for small quantities. Hence, the proposed transmission error-based approach with automated feature selection is able to quantify the degree of slow-speed wear and offers a possible way for condition monitoring and fault diagnosis.

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PHM Survey : Implementation of Signal Processing Methods for Monitoring Bearings and Gearboxes

Cited by 9 publications

References 73 publications

Combining Wavelets and AR Identification for Condition Monitoring of Electric-cam Mechanisms Using PLCopen Readings of Motor Torque

Combining Wavelets and AR Identification for Condition Monitoring of Electric-cam Mechanisms Using PLCopen Readings of Motor Torque

From Corrective to Predictive Maintenance—A Review of Maintenance Approaches for the Power Industry

Condition Monitoring of Slow-speed Gear Wear using a Transmission Error-based Approach with Automated Feature Selection

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