A Framework to Assess Data Quality for Reliability Variables

Hodkiewicz, Melinda; Kelly, Paul; Sikorska, Joanna; Gouws, L.

doi:10.1007/978-1-84628-814-2_15

Cited by 11 publications

(15 citation statements)

References 3 publications

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“…Moreover, the interviews showed that there was still some cynicism around and resistance to the new technology. These findings converge with other evidence which shows that while new technology aided data collectors by providing a list of possible options, these lists also presented challenges [54][55][56] [54,55]. Conversely if the list is detailed and long, they get frustrated trying to differentiate between items and in scrolling down to find the "right" item [56].…”

Section: Summary Of Resultssupporting

confidence: 81%

“…This study uses a sequential mixed-method approach involving interviews with 20 data collectors and a quantitative survey of 109 data collectors in a water utility. It examines the interactive effect of managerial pressure, technological input control and self-concordance on data collection performance.The need to improve the quality of manually-acquired data on assets is wellknown in the reliability literature [1][2][3][4]. Manually-acquired data includes data gathered as a result of inspections, as a part of repair work, and during asset operation by personnel whose main role is to operate or maintain assets.…”

mentioning

confidence: 99%

“…The need to improve the quality of manually-acquired data on assets is wellknown in the reliability literature [1][2][3][4]. Manually-acquired data includes data gathered as a result of inspections, as a part of repair work, and during asset operation by personnel whose main role is to operate or maintain assets.…”

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confidence: 99%

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Are managerial pressure, technological control and intrinsic motivation effective in improving data quality?

Molina

Unsworth

Hodkiewicz

et al. 2013

Reliability Engineering & System Safety

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should be careful of the degree to which "push" factors, such as managerial pressure and technological input control, are relied upon. While they may be helpful for motivating those data collectors who are not intrinsically motivated, they are either not helpful or may discourage those data collectors who are intrinsically motivated. Instead, self-concordance may act as a longer-term, more stable approach to increasing the motivation of data collectors and thus increasing the quality of data that enter reliability systems. This study uses a sequential mixed-method approach involving interviews with 20 data collectors and a quantitative survey of 109 data collectors in a water utility. It examines the interactive effect of managerial pressure, technological input control and self-concordance on data collection performance.The need to improve the quality of manually-acquired data on assets is wellknown in the reliability literature [1][2][3][4]. Manually-acquired data includes data gathered as a result of inspections, as a part of repair work, and during asset operation by personnel whose main role is to operate or maintain assets. These manually collected data are used, in conjunction with sensor data, to develop a picture of asset health and performance which informs decisions about asset renewals, repairs and replacements [5]. Recent integrations and critiques of the data quality literature [6,7] showed that although many proposed solutions to poor quality data have involved cleaning the data once they have been collected [8] many are also aimed at influencing the input of the data -this occurs through either changing external factors that influence the data collector such as managerial and technology structures [e.g., 4, 5], or changing the motivation of the data collector [e.g., 6, 9, 10]. As yet though, little empirical research has rigorously examined the effects of the factors affecting the input of the data. Given the importance of high quality data in reliability systems, and the cost of cleaning the data after their collection, this neglect is surprising. This research therefore empirically tests the effect of the most common three factors (the manager, the technology used in inputting the data, and the intrinsic motivation of the data collector), as well as examining the effect of the interplay between them on the quality of manual data collection.Although there has been considerable increase in the use of sensors and the volume of data collected by them, using operators and maintenance staff (data collectors) to collect data on assets is still a common practice. Manual data 2 collection leverages the experience of the data collector. It often requires them to provide an assessment of the asset's condition, identify a failure mode, or make a prediction as to remaining asset life in addition to recording observations or actions taken. However, when data collectors record their observations consideration needs to be given to psychological factors to ensure that appropriate factors are in place to encoura...

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Section: Summary Of Resultssupporting

confidence: 81%

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confidence: 99%

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Are managerial pressure, technological control and intrinsic motivation effective in improving data quality?

Molina

Unsworth

Hodkiewicz

et al. 2013

Reliability Engineering & System Safety

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“…The peak shift in NG is ar esult of interactions between the stackedg raphene layers, which have at endency to shift the bands to higher frequency,a nd it reveals af ormation of few layer of graphene (between one and four layers) in PJ-AC at 900 8C. [31] Surface area is one of the most important parameters for the accumulationo fc hargec arriers on the surface towards double layer formation across the electrode-electrolyte interface. Thus, N 2 adsorption-desorption studies are conducted along with pore size distribution and presented in Figures 2 and S2.…”

Section: Resultsmentioning

confidence: 86%

Biomass‐Derived Electrode for Next Generation Lithium‐Ion Capacitors

et al. 2016

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We report the fabrication of a carbon-based high energy density Li-ion hybrid electrochemical capacitor (Li-HEC) from low cost and eco-friendly materials. High surface area (2448±20 m(2) g(-1) ) activated carbon (AC) is derived from the environmentally threatening plant, Prosopis juliflora, and used as the positive electrode in a Li-HEC assembly. Natural graphite is employed as negative electrode and electrochemically pre-lithiated prior to the Li-HEC fabrication. The Li-HEC delivers a specific energy of 162.3 Wh kg(-1) and exhibits excellent cyclability (i.e., ∼79 % of initial capacity is retained after 7000 cycles). The superior electrochemical performance of Li-HEC benefits from the tube-like unique structural features of the AC. Also, the presence of a graphitic nanocarbon network improves the ion transport, and the formed micro- and meso-porous network acts as reservoir for the accommodation of charge carriers.

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“…Data quality challenges in field maintenance data collected and stored in the CMMS/EAM are common across nearly all companies and especially in situations where data is manually entered. Discussions on different data quality challenges in maintenance data are well reviewed in (Lukens, Naik, Hu, Doan, & Abado, 2017) (Meeker & Hong, 2014) (Hodkiewicz, Kelly, Sikorska, & Gouws, 2006) (Lin, Gao, Koronios, & Chanana, 2007) (Naik & Saetia, 2018.…”

Section: Maintenance Data Qualitymentioning

confidence: 99%

Best Practices Framework for Improving Maintenance Data Quality to Enable Asset Performance Analytics

Lukens¹,

Naik²,

Saetia³

et al. 2019

PHM_CONF

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Field maintenance data is often captured manually and is prone to have incomplete and inaccurate information in the structured fields. However, unstructured fields captured through work order planning, scheduling, and execution contains a wealth of historical information about asset performance, failure patterns, and maintenance strategies. The prevalent data quality issues in maintenance data need to be understood and processed in order to extract actionable intelligence. This paper describes a best practices framework for measuring and improving data quality, developed through years of research and working with 120+ process and manufacturing organizations. The framework enables evaluating and executing analytics by identifying strengths in the data. It determines where and how asset performance measures such as benchmarking metrics, reliability measures, and bad actor identification can be evaluated with confidence. Missing or inconsistent information can be extracted from the unstructured fields using natural language processing (NLP) techniques to bridge gaps in the analysis. While the NLP algorithms make historical data usable for some analytics, the best practices identify improvements in the work process of capturing data, thereby improving future quality. A feedback on data quality indicators completes the loop to sustain improvements.

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A Framework to Assess Data Quality for Reliability Variables

Cited by 11 publications

References 3 publications

Are managerial pressure, technological control and intrinsic motivation effective in improving data quality?

Are managerial pressure, technological control and intrinsic motivation effective in improving data quality?

Biomass‐Derived Electrode for Next Generation Lithium‐Ion Capacitors

Best Practices Framework for Improving Maintenance Data Quality to Enable Asset Performance Analytics

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