A Data Quality Dashboard for CMMS Data

Gitzel, Ralf; Subbiah, Subanatarajan; Ganz, Christopher

doi:10.5220/0006552501700177

Cited by 5 publications

(6 citation statements)

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“…For most analyses, it is not mandatory to have all the sampled data points values. For instance, to determine a trend, a few missing values in a dataset are not a problem [61]. Larger segments without data are, however, unusable in most analysis.…”

Section: Step 2: Signal Processingmentioning

confidence: 99%

Industrial Data-Driven Processing Framework Combining Process Knowledge for Improved Decision Making—Part 1: Framework Development

Thibault

Kelly²,

Désilets

et al. 2023

Processes

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Data management systems are increasingly used in industrial processes. However, data collected as part of industrial process operations, such as sensor or measurement instruments data, contain various sources of errors that can hamper process analysis and decision making. The authors propose an operating-regime-based data processing framework for industrial process decision making. The framework was designed to increase the quality and take advantage of available process data use to make informed offline strategic business operation decisions, i.e., environmental, cost and energy analysis, optimization, fault detection, debottlenecking, etc. The approach was synthesized from best practices derived from the available framework and improved upon its predecessor by putting forward the combination of process expertise and data-driven approaches. This systematic and structured approach includes the following stages: (1) scope of the analysis, (2) signal processing, (3) steady-state operating periods detection, (4) data reconciliation and (5) operating regime detection and identification. The proposed framework is applied to the brownstock washing department of a dissolving pulp mill. Over a 5-month period, the process was found to be in steady-state 32% of the time. Twenty (20) distinct operating regimes were identified. Further processing with the help of data reconciliation techniques, principal component analysis and k-means clustering showed that the main drivers explaining the operating regimes are the pulp level in tanks, its density, and the shower wash water flow rate. Additionally, it was concluded that the top four persistently problematic sensors across the steady-state spans that would need to be verified are three flow meters (06FIC137, 06FIC152, and 06FIC433), and one consistency sensor (06NIC423). This information was relayed to process experts contacts at the plant for further investigation.

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Section: Step 2: Signal Processingmentioning

confidence: 99%

Industrial Data-Driven Processing Framework Combining Process Knowledge for Improved Decision Making—Part 1: Framework Development

Thibault

Kelly²,

Désilets

et al. 2023

Processes

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“…Data qualification should meet both CMMS and process mining criteria.CMMSSampling size (at least 100 cycles)ConsistencyFree-of-errorCompletenessPlausibility (Gitzel et al. , 2018)Process miningNo Missing data…”

Section: Module Modelmentioning

confidence: 99%

“…CMMSSampling size (at least 100 cycles)ConsistencyFree-of-errorCompletenessPlausibility (Gitzel et al. , 2018)…”

Section: Module Modelmentioning

confidence: 99%

Designing synchronizer module in CMMS software based on lean smart maintenance and process mining

Mazloumi

Moini

Kermani

2022

JQME

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PurposeNew maintenance hypotheses such as lean smart maintenance emphasized internal integration. Since the maintenance process is not fully integrated with other business processes, it indicates that some of the problems in the maintenance process are caused by other departments. Additionally, nothing can be managed or improved without first measuring it. In order to enhance internal integration, this study developed a model that makes use of information systems data to examine synchronization and collaboration across departments engaged in maintenance operations.Design/methodology/approachThis research connects maintenance management and business process management through information systems. A conceptual module model based on CMMS is proposed that will use data which are already available in CMMS and, using process mining, will assess the level of synchronization between departments within an organization.FindingsThis conceptual model will serve as a roadmap for creating better value-added CMMS software. This system operates as a performance measurement tool in three majors, including organizational analysis, workflow analysis and eventually, a future simulation of maintenance processes. This module will serve as a decision support system, highlighting opportunities for improvement in maintenance processes.Originality/valueA practical guideline is provided for the future development of CMMSs and their enhancement to intelligence. All assumptions are based on maintenance theories, techniques for measuring maintenance performance and business process management and process mining.

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“…The works [27], [28] and [29] mathematically approximate some of the metrics proposed in the literature to quantify the quality of the data in time series. However, the mathematical formulation of the mathematical formulation of the complete methodology to measure DQ in time series is still not available in the community of data analysts who must manually adapt their own analysis methodology for DQ to the problem.…”

Section: Related Workmentioning

confidence: 99%

On the Evaluation, Management and Improvement of Data Quality in Streaming Time Series

2022

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A Data Quality Dashboard for CMMS Data

Cited by 5 publications

References 0 publications

Industrial Data-Driven Processing Framework Combining Process Knowledge for Improved Decision Making—Part 1: Framework Development

Industrial Data-Driven Processing Framework Combining Process Knowledge for Improved Decision Making—Part 1: Framework Development

Designing synchronizer module in CMMS software based on lean smart maintenance and process mining

On the Evaluation, Management and Improvement of Data Quality in Streaming Time Series

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