Implementation of Condition Based Maintenance in manufacturing industry - A pilot case study

Rastegari, Ali; Bengtsson, Marcus

doi:10.1109/icphm.2014.7036377

Cited by 19 publications

(15 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The most popular definition of CBM that exists in the literature [7,9] signifies the challenges in the areas of data acquisition, data processing, and decision-making where the CBM implementation and management need refinement. CBM usually requires the selection of components to be monitored in the asset [10]. Therefore, a considerable investment is required to acquire the right equipment for an appropriate data collection setup.…”

Section: Introductionmentioning

confidence: 99%

Failure mode classification for condition-based maintenance in a bearing ring grinding machine

Ahmer

Sandin

Marklund

et al. 2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Technical failures in machines are major sources of unplanned downtime in any production and result in reduced efficiency and system reliability. Despite the well-established potential of Machine Learning techniques in condition-based maintenance (CBM), the lack of access to failure data in production machines has limited the development of a holistic approach to address machine-level CBM. This paper presents a practical approach for failure mode prediction using multiple sensors installed in a bearing ring grinder for process control as well as condition monitoring. Bearing rings are produced in a set of 7 experimental runs, including 5 frequently occurring production failures in the critical subsystems. An advanced data acquisition setup, implemented for CBM in the grinder, is used to capture information about each individual grinding cycle. The dataset is pre-processed and segmented into grinding cycle stages before time and frequency domain feature extraction. A sensor ranking algorithm is proposed to optimize feature selection for failure classification and the installation cost. Random forest models, benchmarked as best performing classifiers, are trained in a two-step classification framework. The presence of failure mode is predicted in the first step and the failure mode type is identified in the second step using the same feature set. Defining the feature set in the failure detection step improves the predictor generalization with the classifiers’ performance accuracy of $$99\%$$ 99 % on the test dataset. The presented approach demonstrates an efficient failure mode classification by selecting crucial sensors resulting in a cost-effective CBM implementation in a bearing ring grinder.

show abstract

Section: Introductionmentioning

confidence: 99%

Failure mode classification for condition-based maintenance in a bearing ring grinding machine

Ahmer

Sandin

Marklund

et al. 2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…Deteriorations or defects of these parts likely lead to changes in the electric currents and/or vibrations emitted by the parts (cf. [46,94,95]).…”

Section: Discussionmentioning

confidence: 99%

Information Fusion of Conflicting Input Data

Mönks

Dörksen

Lohweg

et al. 2016

Sensors

View full text Add to dashboard Cite

Sensors, and also actuators or external sources such as databases, serve as data sources in order to realise condition monitoring of industrial applications or the acquisition of characteristic parameters like production speed or reject rate. Modern facilities create such a large amount of complex data that a machine operator is unable to comprehend and process the information contained in the data. Thus, information fusion mechanisms gain increasing importance. Besides the management of large amounts of data, further challenges towards the fusion algorithms arise from epistemic uncertainties (incomplete knowledge) in the input signals as well as conflicts between them. These aspects must be considered during information processing to obtain reliable results, which are in accordance with the real world. The analysis of the scientific state of the art shows that current solutions fulfil said requirements at most only partly. This article proposes the multilayered information fusion system MACRO (multilayer attribute-based conflict-reducing observation) employing the μBalTLCS (fuzzified balanced two-layer conflict solving) fusion algorithm to reduce the impact of conflicts on the fusion result. The performance of the contribution is shown by its evaluation in the scope of a machine condition monitoring application under laboratory conditions. Here, the MACRO system yields the best results compared to state-of-the-art fusion mechanisms. The utilised data is published and freely accessible.

show abstract

“…Several papers have been published in the past decade on fault diagnosis and repair strategies using various frameworks, concepts, simulation for modeling theory, and the use of expert systems and KBSs in various international journals and conferences. The works of Wang et al [3], Jardine et al [4], Tao et al [5], Rastegari et al [6], Ahmad et al [7], Lee and Scott [8], Veldman et al [9], Derigent et al [10] and Koochaki et al [11] were addressed maintenance dependent conditionbased policies. Iung et al [12], Bousdekis et al [13], Lee J et al [14], Chiang et al [15], Zhao et al [16], Gupta et al [17], Yang et al [18] and Alrabghi et al [19] were focused on the development of maintenance models for evaluation and optimal throughput.…”

Section: Literature Reviewmentioning

confidence: 99%