Introduction to Condition Monitoring in Mechanical and Electrical Systems

Marwala, Tshilidzi

doi:10.1007/978-1-4471-2380-4_1

Cited by 4 publications

(4 citation statements)

References 88 publications

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“…For example, the WVD is useful in extracting transient features in time-frequency signals but cannot account for the local transient features of the signals at a given time and presents cross-terms when signal with many frequency components is being analysed. Moreover, WVD spreads noise and may result in negative amplitude values which are irrelevant leading to further complications (Cohen 1989;Marwala 2012). As opposed to the WVD, the STFT is useful in extracting the localised transient feature by transforming a small-time window into a frequency domain.…”

Section: 1mentioning

confidence: 99%

Hybrid intelligent predictive maintenance model for multiclass fault classification

et al. 2023

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Data recorded from monitoring the health condition of industrial equipment are often high-dimensional, nonlinear, nonstationary and characterised by high levels of uncertainty. These factors limit the efficiency of machine learning techniques to produce desirable results when developing effective fault classification frameworks. This paper sought to propose a hybrid artificial intelligent predictive maintenance model based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Principal Component Analysis (PCA) and Least Squares Support Vector Machine (LSSVM) optimised by the combination of Coupled Simulated Annealing and Nelder-Mead Simplex optimisation algorithms (ICEEMDAN-PCA-LSSVM). Here, ICEEMDAN was first employed as a denoising technique to decompose signals into series of Intrinsic Mode Functions (IMFs) of which only relevant IMFs containing the relevant fault features were retained for signal reconstruction. PCA was then employed as a dimension reduction technique through which the resulting set of uncorrelated features extracted served as input for LSSVM for classifying various fault types. The proposed technique is compared with three established methods (Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and Artificial Neural Network (ANN)) with multiclass classification capabilities. The various techniques were tested on an experimental UCI machine learning benchmark data obtained from multi-sensors of a hydraulic test rig. The results from the analysis revealed that the proposed ICEEMDAN-PCA-LSSVM technique is versatile and outperformed all the compared classifiers in terms of accuracy, error rate and other evaluation metrics considered. The proposed hybrid technique drastically reduced the redundancies and the dimension of features, allowing for the efficient consideration of relevant features for the enhancement of classification accuracy and convergence speed.

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Section: 1mentioning

confidence: 99%

Hybrid intelligent predictive maintenance model for multiclass fault classification

et al. 2023

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“…The main purpose of condition monitoring is to evaluate the health state and to estimate the residual life of a component or system being monitored [23]. It consists of fault detection, diagnosis, and prognosis stages [24,25]. The fault detection stage, is the process of recognizing faulty state in the monitored system through signs and symptoms.…”

Section: Fault Size Estimation Approach and Remaining Useful Life Pre...mentioning

confidence: 99%

A Review of Ball Bearings Fault Size Estimation (FSE), Fault Degradation Estimation (FDE), and Artificial Intelligence Based Approaches during Prognosis

Hlatshwayo

Madushele

Ahmed

2021

Advances in Science and Technology

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Ball bearings are critical components of any industrial rotary equipment. They constitute about 90% of industrial machines’ components – and are thus responsible for the largest proportion of failures – approximately 70-85% of downtime. Defected bearings, while in service, give rise to high vibration amplitudes in rotary equipment, resulting in great reduction in their operational efficiency coupled with high energy consumption. Their premature and inadvertent failure could result in unplanned equipment downtown – thereby causing production loss and increased maintenance cost. Patently, to curtail this, it is vital that their health state is monitored throughout their service life for early faults detection, diagnosis, and prognosis. A knowledge of when a bearing will fail – that is, its remaining useful life (RUL) – can serve as supplement to maintenace decision-making such as determining in advance the time an equipment needs to be taken out-of-service and that can alternatively allow for sufficient lead time for maintenance planning as well. This can correspondingly result in enhancement in rotary systems effectiveness – i.e., availability, reliability, maintainability, and capability. Three popular condition monitoring approaches are signal processing-based approaches namely fault size estimation (FSE) and fault degradation estimation (FDE) as well as artifial intelligent (AI) based approach. It is, however, still a challenge to estimate a bearing fault size and therefore its RUL with high precision based on what has been diagnosed using these approaches. Accordingly, this review holistically explore capabilities and limitations of these approaches from recently published work. The reviewed limations are summarized and serve as new research avenue.

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“…Condition monitoring (CM) is integral to the efficient operation of production systems. By recognizing the need for maintenance activities, production downtimes are minimized [1]. The purpose of corresponding condition monitoring systems (CMS) is to gather relevant data and offer insight on the current state of critical machine components and the likely progression of their condition.…”

Section: Motivationmentioning

confidence: 99%

An Information Processing Framework Facilitating the Implementation of Condition Monitoring in Cyber-Physical Systems

Fleischmann

Kißkalt

Krusemark

et al. 2018

AMM

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Implementing condition monitoring functionality in production machinery often proves to be a difficult task. Device-and process-specific algorithms must be created while inhomogeneous industrial communication networks hinder the integration of control signals and process variables. Further challenges arise from the advance of flexible Cyber-Physical Systems (CPS) and the Industrial Internet of Things (IIoT). They demand a service-oriented condition monitoring architecture, which seamlessly adapts to quickly changing production topologies. Existing condition monitoring systems (CMS) and reference architectures for CMS however do not possess the capabilities to meet the requirements originating from CPS and the IIoT. This paper presents a software module serving as a generic framework to ease the implementation of decentralized condition monitoring functionalities. A decentralized component, the monitoring module, constitutes a part of a holistic condition monitoring architecture managed by a central server deployed on an edge or cloud server. Therefore, the monitoring module offers an interface through which its data processing flow and algorithms are entirely remotely configurable during operation. Algorithms are encapsulated in function blocks, which can easily be setup and interconnected. Scalability is ensured by the use of web technology optimized for efficient data handling and parallel data-processing. The examined use cases show the potential of the developed software module for its deployment in the use case of monitoring turn-mill-centers.

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Introduction to Condition Monitoring in Mechanical and Electrical Systems

Cited by 4 publications

References 88 publications

Hybrid intelligent predictive maintenance model for multiclass fault classification

Hybrid intelligent predictive maintenance model for multiclass fault classification

A Review of Ball Bearings Fault Size Estimation (FSE), Fault Degradation Estimation (FDE), and Artificial Intelligence Based Approaches during Prognosis

An Information Processing Framework Facilitating the Implementation of Condition Monitoring in Cyber-Physical Systems

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