Development of a Framework to Aid the Transition from Reactive to Proactive Maintenance Approaches to Enable Energy Reduction
The disparity between public datasets and real industrial datasets is limiting the practical application of advanced data analysis. Therefore, industry is stuck in a reactive mode regarding their maintenance strategy and cannot transition to cost-effective and energy-efficient proactive maintenance...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/13/6704 |
| _version_ | 1797480805671370752 |
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| author | Michael Ahern Dominic T. J. O’Sullivan Ken Bruton |
| author_facet | Michael Ahern Dominic T. J. O’Sullivan Ken Bruton |
| author_sort | Michael Ahern |
| collection | DOAJ |
| description | The disparity between public datasets and real industrial datasets is limiting the practical application of advanced data analysis. Therefore, industry is stuck in a reactive mode regarding their maintenance strategy and cannot transition to cost-effective and energy-efficient proactive maintenance approaches. In this paper, an integration-type adaptation of the CRISP-DM data mining process model is proposed to combine domain expertise with data science techniques to address the pervasive data issues in industrial datasets. The development of the Industrial Data Analysis Improvement Cycle (IDAIC) framework led to the novel repurposing of knowledge-based fault detection and diagnosis (FDD) techniques for data quality assessment. Through interdisciplinary collaboration, the proposed framework facilitates a transition from reactive to proactive problem solving by firstly resolving known faults and data issues using domain expertise, and secondly exploring unknown or novel faults using data analysis. |
| first_indexed | 2024-03-09T22:05:27Z |
| format | Article |
| id | doaj.art-4cfe92f11f3a41619cfcacbc6d104057 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:05:27Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4cfe92f11f3a41619cfcacbc6d1040572023-11-23T19:41:17ZengMDPI AGApplied Sciences2076-34172022-07-011213670410.3390/app12136704Development of a Framework to Aid the Transition from Reactive to Proactive Maintenance Approaches to Enable Energy ReductionMichael Ahern0Dominic T. J. O’Sullivan1Ken Bruton2Intelligent Efficiency Research Group (IERG), Department of Civil and Environmental Engineering, University College Cork, T12 CY82 Cork, IrelandIntelligent Efficiency Research Group (IERG), Department of Civil and Environmental Engineering, University College Cork, T12 CY82 Cork, IrelandIntelligent Efficiency Research Group (IERG), Department of Civil and Environmental Engineering, University College Cork, T12 CY82 Cork, IrelandThe disparity between public datasets and real industrial datasets is limiting the practical application of advanced data analysis. Therefore, industry is stuck in a reactive mode regarding their maintenance strategy and cannot transition to cost-effective and energy-efficient proactive maintenance approaches. In this paper, an integration-type adaptation of the CRISP-DM data mining process model is proposed to combine domain expertise with data science techniques to address the pervasive data issues in industrial datasets. The development of the Industrial Data Analysis Improvement Cycle (IDAIC) framework led to the novel repurposing of knowledge-based fault detection and diagnosis (FDD) techniques for data quality assessment. Through interdisciplinary collaboration, the proposed framework facilitates a transition from reactive to proactive problem solving by firstly resolving known faults and data issues using domain expertise, and secondly exploring unknown or novel faults using data analysis.https://www.mdpi.com/2076-3417/12/13/6704data analyticsdata miningfault detection and diagnosticsindustrial AIdata qualitybuilding AFDD |
| spellingShingle | Michael Ahern Dominic T. J. O’Sullivan Ken Bruton Development of a Framework to Aid the Transition from Reactive to Proactive Maintenance Approaches to Enable Energy Reduction Applied Sciences data analytics data mining fault detection and diagnostics industrial AI data quality building AFDD |
| title | Development of a Framework to Aid the Transition from Reactive to Proactive Maintenance Approaches to Enable Energy Reduction |
| title_full | Development of a Framework to Aid the Transition from Reactive to Proactive Maintenance Approaches to Enable Energy Reduction |
| title_fullStr | Development of a Framework to Aid the Transition from Reactive to Proactive Maintenance Approaches to Enable Energy Reduction |
| title_full_unstemmed | Development of a Framework to Aid the Transition from Reactive to Proactive Maintenance Approaches to Enable Energy Reduction |
| title_short | Development of a Framework to Aid the Transition from Reactive to Proactive Maintenance Approaches to Enable Energy Reduction |
| title_sort | development of a framework to aid the transition from reactive to proactive maintenance approaches to enable energy reduction |
| topic | data analytics data mining fault detection and diagnostics industrial AI data quality building AFDD |
| url | https://www.mdpi.com/2076-3417/12/13/6704 |
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