Fleet-Wide Proactive Maintenance of Machine Tools

Peysson, Flavien; Mozzati, Christophe; León, David De; Lafuste, Quentin; Léger, Jean-Baptiste

doi:10.1007/978-3-030-02203-7_13

Cited by 5 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A knowledge structure diagram of fleets in the machine field is addressed on the basis of ontologies for diagnostic goals, offering participants with more knowledge and integrated messages regarding the behaviors of system. 42,43 A method of smart problem diagnosis is proposed on the basis of ontology, which revealed an instance of smart problem diagnosis principles. 44,45 A framework of ontological pattern is proposed to get a universal knowledge representation for machine state in factory.…”

Section: Related Workmentioning

confidence: 99%

Study on an architecture of ontology-based task modeling and deduction for automotive troubleshooting service

Liang

2022

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

The rapid development of today’s smart automobile with increasing functionality and complexity, which results in rising requirement and cost for its troubleshooting service in the after-sales stage. Hence, to keep the firm’s competitive strength, effective design knowledge utilization of automotive created from the task model can promote the feasibility making of a troubleshooting procedure by offering available relationships and semantic scheme of task. The proposed architecture primarily consists of base, field and application three tiers. A formalized representation of ontology, OWL, is used to organize the base filed. The filed tier includes extensional notions and relations for integration of troubleshooting and a criterion repository for verifying solution feasibility, which is depicted in SWRL. In the application tier, a deducing module is generated on the basis of ontology and criterion deduction. To enhance this semantics, in this research, a task modeling and deduction mechanism with feature-intensive ontology are proposed to clearly represent correlative notions for automotive troubleshooting planning (ATP). A criterion-based deducing module on the basis of OWL-DL and SWRL is also applied to specify implied relations through merging deducing modules (DMs) to deal with complex feature data. Eventually, this proposed architecture is examined and verified with an instance relevant to automotive troubleshooting procedure.

show abstract

Section: Related Workmentioning

confidence: 99%

Study on an architecture of ontology-based task modeling and deduction for automotive troubleshooting service

Liang

2022

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

show abstract

“…Similarly, Liu (2018) [37] proposed a CPS framework for health management of wind turbines and demonstrated the health assessment using fleet-based analytics. Peysson et al (2019) [41] has recently presented a framework for fleet-wide maintenance. Within our paper, we prefer the use of the term "multimachine analysis", rather than "fleet" since the latter term inevitable leads one to think more large scale when it comes tothe number of machines whilst our framework can provide significant insights with as little as two machines.…”

Section: Related Workmentioning

confidence: 99%

Multi-Machine Gaussian Topic Modeling for Predictive Maintenance

2021

View full text Add to dashboard Cite

In this paper, we propose a coherent framework for multi-machine analysis, using a group clustering model, which can be utilized for predictive maintenance (PdM). The framework benefits from the repetitive structure posed by multiple machines and enables for assessment of health condition, degradation modeling and comparison of machines. It is based on a hierarchical probabilistic model, denoted Gaussian topic model (GTM), where cluster patterns are shared over machines and therefore it allows one to directly obtain proportions of patterns over the machines. This is then used as a basis for cross comparison between machines where identified similarities and differences can lead to important insights about their degradation behavior. The framework is based on aggregation of data over multiple streams by a predefined set of features extracted over a time window. Moreover, the framework contains a clustering schema which takes uncertainty of cluster assignments into account and where one can specify a desirable degree of reliability of the assignments. By using a multi-machine simulation example, we highlight how the framework can be utilized in order to obtain cluster patterns and inherent variations of such patterns over machines. Furthermore, a comparative study with the commonly used Gaussian mixture model (GMM) demonstrates that GTM is able to identify inherent patterns in the data while the GMM fails. Such result is a consequence of the group level being modeled by the GTM while being absent in the GMM. Hence, the GTM are trained with a view on the data that is not available to the GMM with the consequence that the GMM can miss important, possibly even key, cluster patterns. Therefore, we argue that more advanced cluster models, like the GTM, can be key for interpreting and understanding degradation behavior across machines and ultimately for obtaining more efficient and reliable PdM systems.INDEX TERMS exploratory data analysis, cluster analysis, Gaussian topic modeling, hierarchical modeling, multi-machine analysis, multiple data streams, predictive maintenance

show abstract

“…Several previous approaches have been developed for fault detection, diagnostics, prognostics and maintenance for fleets of systems, with the focus on transferring information between units of a fleet [2]- [8]. Most of the approaches have been developed for a supervised set-up where the main challenge is learning and transferring the relevant fault specific patterns or degradation behaviour between units.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Fault Detection in Varying Operating Conditions

Michau

Fink

2019

2019 IEEE International Conference on Prognostics and Health Management (ICPHM)

View full text Add to dashboard Cite

Training data-driven approaches for complex industrial system health monitoring is challenging. When data on faulty conditions are rare or not available, the training has to be performed in a unsupervised manner. In addition, when the observation period, used for training, is kept short, to be able to monitor the system in its early life, the training data might not be representative of all the system normal operating conditions. In this paper, we propose five approaches to perform fault detection in such context. Two approaches rely on the data from the unit to be monitored only: the baseline is trained on the early life of the unit. An incremental learning procedure tries to learn new operating conditions as they arise. Three other approaches take advantage of data from other similar units within a fleet. In two cases, units are directly compared to each other with similarity measures, and the data from similar units are combined in the training set. We propose, in the third case, a new deep-learning methodology to perform, first, a feature alignment of different units with an Unsupervised Feature Alignment Network (UFAN). Then, features of both units are combined in the training set of the fault detection neural network. The approaches are tested on a fleet comprising 112 units, observed over one year of data. All approaches proposed here are an improvement to the baseline, trained with two months of data only. As units in the fleet are found to be very dissimilar, the new architecture UFAN, that aligns units in the feature space, is outperforming others.

show abstract

Fleet-Wide Proactive Maintenance of Machine Tools

Cited by 5 publications

References 6 publications

Study on an architecture of ontology-based task modeling and deduction for automotive troubleshooting service

Study on an architecture of ontology-based task modeling and deduction for automotive troubleshooting service

Multi-Machine Gaussian Topic Modeling for Predictive Maintenance

Unsupervised Fault Detection in Varying Operating Conditions

Contact Info

Product

Resources

About