Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering

Díaz-Rozo, Javier; Bielza, Concha; Larrañaga, Pedro

doi:10.1016/j.engappai.2019.103434

Cited by 24 publications

(8 citation statements)

References 20 publications

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“…Mixture models, especially Gaussian Mixture Model (GMM), are a widely used statistical method as an effective universal approximator. Justifiably, it finds use in several applications (Pimentel, Clifton, Clifton & Tarassenko, 2014;Diaz-Rozo, Bielza & Larrañaga, 2020) such as density estimation, clustering, association rules, outlier detection, latent factors, ranking, and even data visualization. Given its wide use, effective training of GMM is a continuously evolving area (Jin, Zhang, Balakrishnan, Wainwright & Jordan, 2016;Kurban, Jenne, & Dalkilic, 2017) with Expectation Maximization (EM) being one of the popular methods (Ververidis & Kotropoulos, 2008;Balakrishnan, Wainwright & Yu, 2017;Zhao, Li & Sun, 2020).…”

Section: Proposed Methodsmentioning

confidence: 99%

Collaborative Prognostics for Machine Fleets Using a Novel Federated Baseline Learner

2021

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Difficulty in obtaining enough run-to-fail datasets is a major barrier that impedes the widespread acceptance of Prognostic and Health Management (PHM) technology in many applications. Recent progress in federated learning demonstrates great potential to overcome such difficulty because it allows one to train PHM models based on distributed databases without direct data sharing. Therefore, this technology can overcome local data scarcity challenges by training the PHM model based on multi-party databases. To demonstrate the ability of federated learning to enhance the robustness and reliability of PHM models, this paper proposes a novel federated Gaussian Mixture Model (GMM) algorithm to build universal baseline models based on distributed databases. A systematic methodology to perform collaborative prognostics is further presented using the proposed federated GMM algorithm. The usefulness and performance are validated through a simulated dataset and the NASA Turbofan Engine Dataset. The proposed federated approach with parameter sharing is shown to perform at par with the traditional approach with data sharing. The proposed model further demonstrates improved robustness of predictions made collaboratively keeping the data private compared to local predictions. Federated collaborative learning can serve as a catalyst for the adaptation of business models based on the servitization of assets in the era of Industry 4.0. The methodology facilitates effective learning of asset health conditions for data-scarce organizations by collaborating with other organizations preserving data privacy. This is most suitable for a servitization model for Overall Equipment Manufacturers who sell to multiple organizations.

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Section: Proposed Methodsmentioning

confidence: 99%

Collaborative Prognostics for Machine Fleets Using a Novel Federated Baseline Learner

2021

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“…Wang et al [28] gave a 3D printing thermal deformation prediction method based on a Gaussian mixed model for compensation of deformation during processing. Javier Diaz-Rozo et al [29] proposed a machine tool condition monitoring method based on Gaussian dynamic probabilistic clustering.…”

Section: Application Of Probabilistic Model In Manufacturingmentioning

confidence: 99%

A Deformation Force Monitoring Method for Aero-Engine Casing Machining Based on Deep Autoregressive Network and Kalman Filter

Guo

Liu

et al. 2022

Applied Sciences

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Aero-engine casing is a kind of thin-walled rotary part for which serious deformation often occurs during its machining process. As deformation force is an important physical quantity associated with deformation, the utilization of deformation force to control the deformation has been suggested. However, due to the complex machining characteristics of an aero-engine casing, obtaining a stable and reliable deformation force can be quite difficult. To address this issue, this paper proposes a deformation force monitoring method via a pre-support force probabilistic decision model based on deep autoregressive neural network and Kalman filter, for which a set of sophisticated clamping devices with force sensors are specifically developed. In the proposed method, the pre-support force is determined by the predicted value of the deformation force and the equivalent flexibility of the part, while the measurement errors and the reality gaps are reduced by Kalman filter via fusing the predicted and measured data. Both computer simulation and physical machining experiments are carried out and their results give a positive confirmation on the effectiveness of the proposed method. The results are as follows. In the simulation experiments, when the confidence is 84.1%, the success rate of deformation force monitoring is increased by about 30% compared with the traditional approach, and the final impact of clamping deformation of the proposed method is less than 0.003 mm. In the real machining experiments, the results show that the calculation error of deformation by the proposed method based on monitoring the deformation force is less than 0.008 mm.

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“…Model accuracy is reduced as ball-bearings have different failure modes, increasing the difficulty of training these models before deployment. Additionally, ball-bearing degradation negatively affects the algorithm performance, reducing its fitting to the data stream [6].…”

Section: Introductionmentioning

confidence: 99%

“…For the online analysis in this article, the Page sequential test and Chernoff-bounds are applied as a concept drift detection methodology as in [6]. Also, a novel health index (HI) is proposed for ball-bearing monitoring.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric HMMs for Online Ball-Bearing Health Assessments

Puerto-Santana¹,

Bielza

Díaz-Rozo³

et al. 2022

IEEE Internet Things J.

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The degradation of critical components inside large industrial assets, such as ball-bearings, has a negative impact on production facilities, reducing the availability of assets due to an unexpectedly high failure rate. Machine learningbased monitoring systems can estimate the remaining useful life (RUL) of ball-bearings, reducing the downtime by early failure detection. However, traditional approaches for predictive systems require run-to-failure (RTF) data as training data, which in real scenarios can be scarce and expensive to obtain as the expected useful life could be measured in years. Therefore, to overcome the need of RTF, we propose a new methodology based on online novelty detection and asymmetrical hidden Markov models (As-HMM) to work out the health assessment. This new methodology does not require previous RTF data and can adapt to natural degradation of mechanical components over time in data-stream and online environments. As the system is designed to work online within the electrical cabinet of machines it has to be deployed using embedded electronics. Therefore, a performance analysis of As-HMM is presented to detect the strengths and critical points of the algorithm. To validate our approach, we use real life ball-bearing data-sets and compare our methodology with other methodologies where no RTF data is needed and check the advantages in RUL prediction and health monitoring. As a result, we showcase a complete end-to-end solution from the sensor to actionable insights regarding RUL estimation towards maintenance application in real industrial environments.

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Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering

Cited by 24 publications

References 20 publications

Collaborative Prognostics for Machine Fleets Using a Novel Federated Baseline Learner

Collaborative Prognostics for Machine Fleets Using a Novel Federated Baseline Learner

A Deformation Force Monitoring Method for Aero-Engine Casing Machining Based on Deep Autoregressive Network and Kalman Filter

Asymmetric HMMs for Online Ball-Bearing Health Assessments

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