A hidden-Gamma model-based filtering and prediction approach for monotonic health factors in manufacturing

Susto, Gian Antonio; Schirru, Andrea; Pampuri, Simone; Beghi, Alessandro; Nicolao, Giuseppe De

doi:10.1016/j.conengprac.2018.02.011

Cited by 40 publications

(16 citation statements)

References 62 publications

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“…A deep learning image detection module identifies different machines and IoT allows the machines to report machine settings and machine states to a cloud-based server [36]. Susto et al describe a methodology to derive the health factor of machines by applying a Monte Carlo approach based on particle filtering techniques to a real industrial predictive maintenance problem in the semiconductor industry [37]. Wang and Wang discuss the impact of AI on the future of predictive maintenance by focusing on DL technology.…”

Section: Predictive Maintenancementioning

confidence: 99%

“…Thereby, a multitude of studies suggests the application of recent AL, ML, and DL approaches for the continuous reporting of machine settings, machine states, and quality parameter settings. Based on real-time data, enhanced knowledge can be used for further predictive analysis regarding a strategic and pro-active plant maintenance strategy for production and logistics processes [36][37][38][39][40][41].…”

Section: Framework For the Application Ai ML And Dl In Smart Logisticsmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Woschank¹,

Rauch

Zsifkovits³

2020

Sustainability

195

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Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

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Section: Predictive Maintenancementioning

confidence: 99%

Section: Framework For the Application Ai ML And Dl In Smart Logisticsmentioning

confidence: 99%

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Woschank¹,

Rauch

Zsifkovits³

2020

Sustainability

195

View full text Add to dashboard Cite

show abstract

“…Gaussian process regression (Pan et al (2016)), support/relevance vector machine (Chen es al. (2018); Leahy et al(2018)), gamma/wiener process (Susto et al (2018); Zhang et al (2018)), etc. Data-driven methods also include artificial neural network (ANN) models which learn the mapping between feature vectors and the associated RUL values.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Data-driven Prognostics Models: A Process Perspective

Li¹,

Verhagen²,

Curran³

2019

Proceedings of the 29th European Safety and Reliability Conference (ESREL)

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Remaining useful life (RUL) prediction is crucial for the implementation of Prognostics and Health Management (PHM) systems, enabling application of predictive maintenance strategies for critical systems (e.g. in aviation, power, railway). Existing literature addresses aspects of data-driven prognostic approaches, with a predominant focus on introducing and testing various novel prediction techniques which are purposed towards improving prediction accuracy performance. However, a relative lack of research can be identified when considering a comparative evaluation of competing for data-driven approaches. In particular, the contributing process elements and characteristics of data-driven prognostics methods are typically not compared in detail. To overcome these drawbacks, this paper aims to evaluate the underlying technical processes for statistical and artificial neural networks (ANN) methods for prognostics. A case study is conducted to implement both approaches on the PHM08 Challenge Data Set for comparison. This research comprehensively compares the statistical and ANN prognostic methods in a systematic manner, covering and comparing their respective technical processes, and evaluates the results with respect to prediction accuracy.

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“…In [3] different mathematical models or artificial intelligence (AI) algorithms have been developed for different conditions. The taxonomies of the physical, statistical, and machine learning methods [9,10] are summarized in Figure 1.Energies 2019, 12, x FOR PEER REVIEW 2 of 18 health management (PHM) are obtaining a lot of attention recently, as evidenced by the literature. In particular, condition-based maintenance (CBM), which is usually applied in practice [3], consists of data collection, data processing, and maintenance decision-making [4].…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Data Science for Vibration Heteroscedasticity and Predictive Maintenance of Rotary Bearings

et al. 2019

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Electric motors are widely used in our society in applications like cars, household appliances, industrial equipment, etc. Costly failures can be avoided by establishing predictive maintenance (PdM) policies or mechanisms for the repair or replacement of the components in electric motors. One of key components in the motors are bearings, and it is critical to measure the key features of bearings to support maintenance decision. This paper proposes a data science approach with embedded statistical data mining and a machine learning algorithm to predict the remaining useful life (RUL) of the bearings in a motor. The vibration signals of the bearings are collected from the experimental platform, and fault detection devices are developed to extract the important features of bearings in time domain and frequency domain. Regression-based models are developed to predict the RUL, and weighted least squares regression (WLS) and feasible generalized least squares regression (FGLS) are used to address the heteroscedasticity problem in the vibration dataset. Support vector regression (SVR) is also applied for prediction benchmarking. Case studies show that the proposed data science approach handled large datasets with ease and predicted the RUL of the bearings with accuracy. The features extracted from time domain are more significant than those extracted from frequency domain, and they benefit engineering knowledge. According to the RUL results, the PdM policy is developed for component replacement at the right moment to avoid the catastrophic equipment failure.

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A hidden-Gamma model-based filtering and prediction approach for monotonic health factors in manufacturing

Cited by 40 publications

References 62 publications

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Comparison of Data-driven Prognostics Models: A Process Perspective

Data Science for Vibration Heteroscedasticity and Predictive Maintenance of Rotary Bearings

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