Challenges in predictive maintenance – A review

Nunes, Pedro; Santos, José; Breda, Ana

doi:10.1016/j.cirpj.2022.11.004

Cited by 71 publications

(32 citation statements)

References 99 publications

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“…In order to ensure the continuity and safety of industrial production while reducing production costs, it is necessary to perform predictive maintenance on mechanical seals [8]. An effective maintenance strategy can avoid unplanned production stops, reduce costs, and possibly even extend the life of mechanical seals [9].…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Tribological behavior characterization, fault detection and health evaluation of mechanical seals based on face vibration acceleration measurement

Wang

Song

Li³

et al. 2023

Preprint

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Aiming at the performance degradation caused by the wear of the face of the contacting mechanical seal during operation, and the lack of effective monitoring methods and evaluation indicators for predictive maintenance, a mechanical seal test rig was built. The vibration and closing force signals of the seal face were collected. The relationship between the closing force with the phase change law and the performance degradation of the face was clarified. The vibration characteristic parameters of the face were studied and the vibration sensitive characteristics of the time domain, frequency domain and time-frequency domain were screened. The incipient fault detection method and degradation assessment method of mechanical seals were studied. The results show that the circumferential variation of the closing force can characterize the performance degradation degree of the mechanical seal. With the increase of the face wear, the maximum face closing force changes from regular to random with the phase. The mean value, fuzzy entropy, and permutation entropy of the vibration signal of the seal face can characterize the degree of performance degradation of the mechanical seal. The incipient fault time of the mechanical seal can be identified by the incipient fault detection model. The health status of the mechanical seal can be clearly divided by the performance degradation assessment model, which provides a basis for predictive maintenance of the mechanical seal.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Tribological behavior characterization, fault detection and health evaluation of mechanical seals based on face vibration acceleration measurement

Wang

Song

Li³

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…In addition to the incorporation of Wasserstein loss, efforts were made to constrain WGAN by enforcing weight clipping. This involved maintaining constant values in discriminator weights, imposing Lipschitz limitations, as showcased in equation (1),…”

Section: Wgan-gpmentioning

confidence: 99%

“…methods often prove inadequate in meeting the rigorous demands for reliability and safety inherent in modern engineering systems. Thus, predictive health management (PHM) has become increasingly important [1,2]. The key to implementing PHM lies in real-time monitoring of systems through intelligent sensors, coupled with data processing and analysis techniques aimed at extracting valuable health indicators (HI).…”

Section: Introductionmentioning

confidence: 99%

Construction health indicator using physically-informed 1D-WGAN-GP joint attention LSTM-DenseNet method

Yang,

Sun

et al. 2024

Meas. Sci. Technol.

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In data-driven prognosis methods, the accuracy of predicting the remaining useful life (RUL) of mechanical systems is predominantly contingent upon the efficacy of system health indicators, typically amalgamated from statistical features derived from collected signals. Nevertheless, the majority of extant health indicators are beset by two principal shortcomings: (1) During traditional data denoising processes, degradation information from raw data is prone to loss owing to the lack of incorporation of the true physical properties of the data; and (2) The performance evaluation of constructed health indicators is imbalanced due to the influence of network structures on single models, often resulting in strong performance in only one or two indicators. To overcome such shortcomings, a mechanical health indicator construction method based on physical properties was proposed, termed 1D-WGAN-GP Joint Attention LSTM-DenseNet (ALD). Firstly, artificial sample data is generated by analysing the physical properties of the original dataset, which is then used to train the 1D-WGAN-GP model to achieve data denoising. Subsequently, the fusion of the Attention LSTM (A-LSTM) network and DenseNet network is utilised to extract crucial feature vectors of health indicators under varying health conditions from the denoised data. Finally, the extracted feature vectors are used to construct system health indicators using the Euclidean distance method, and these indicators are used for predicting the system's RUL. The results indicate that the proposed method outperformed traditional methods in terms of denoising effectiveness. Further, through ablation experiment analysis, the health indicators constructed by the proposed method demonstrated obvious complementarity in terms of monotonicity, correlation, robustness, and comprehensive evaluation. In RUL prediction applications, the proposed method also exhibited good performance, thereby validating its effectiveness.

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“…The article [20] focuses on challenges in establishing comprehensive data-driven systems for PdM. It addresses issues like noisy data, model generalizability, and data collection in industrial settings.…”

Section: Related Workmentioning

confidence: 99%

A survey on AI Approaches for Internet of Things Devices Failure Prediction

Khattach,

Moussaoui,

Hassine

2023

E3S Web of Conf.

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The use of Internet of Things (IoT) devices has experienced a substantial surge in various sectors, including manufacturing, healthcare, agriculture, and transportation. Nonetheless, the susceptibility of these devices to failures has emerged as a significant concern, contributing to costly periods of inactivity and diminished productivity. Consequently, the development of sophisticated and precise techniques for forecasting device failures in advance has become imperative. This research paper thoroughly investigates and analyses the most recent advancements and scholarly inquiries pertaining to the implementation of artificial intelligence methodologies, notably machine learning and deep learning, in the realm of predicting and averting IoT device failures. These AI-based approaches can be trained on extensive historical datasets, enabling the detection of distinctive patterns and anomalies that serve as potential precursors to device malfunctions. By incorporating these innovative failure prediction techniques into their operations, organizations can actively identify and address potential issues, thereby minimizing the adverse repercussions of device failures on their overall performance and functionality.

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Challenges in predictive maintenance – A review

Cited by 71 publications

References 99 publications

WITHDRAWN: Tribological behavior characterization, fault detection and health evaluation of mechanical seals based on face vibration acceleration measurement

WITHDRAWN: Tribological behavior characterization, fault detection and health evaluation of mechanical seals based on face vibration acceleration measurement

Construction health indicator using physically-informed 1D-WGAN-GP joint attention LSTM-DenseNet method

A survey on AI Approaches for Internet of Things Devices Failure Prediction

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