Structuring Data for Intelligent Predictive Maintenance in Asset Management

Aremu, Oluseun Omotola; Palau, Adrià Salvador; Parlikad, Ajith Kumar; Hyland-Wood, David; McĂree, P. R.

doi:10.1016/j.ifacol.2018.08.370

Cited by 28 publications

(11 citation statements)

References 21 publications

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“…However, a key challenge with machine learning in prognostics health monitoring based on analysis of asset condition data is the handling of high dimensional data sets. Using domain knowledge that faults and their implications correlate to a type of information contained in an asset's life cycle data and are translatable to a type of domain knowledge representation with an entropy measure can be utilized to provide a dimension reduction framework [192]. Alternative approaches for dimension reduction using principal components with machine learning have been identified by Gorban and Zinovyev [193].…”

Section: A Brief Discussion On Applications Of Big Data Analytics Fomentioning

confidence: 99%

Requirements for Big Data Adoption for Railway Asset Management

2020

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Nowadays, huge amounts of data have been captured along with the day-today operation of assets including railway systems. Hence, we have come to the era of big data. The utilization of big data technologies for asset condition information management is becoming indispensable for improving asset management decision making. The vital information such as precursor information collected on failure modes and knowledge that may be available for analysis is hidden within the large extent of data. There are analysis tools incorporated with techniques such as multiple regression analysis and machine learning that are facilitated by the availability of big data. Therefore, the utilization of big data technologies for asset condition information management is becoming indispensable for improving asset management decision making. This paper provides a review of the requirements and challenges for big data analytics applications to railway asset management. The review focuses on railway asset data collection, data management, data applications with the implementation of Blockchain technology as well as big data analytics technologies. The need for, and the importance of big data analytics in railway asset management; and the requirement for the asset condition data collection in the railway industry are highlighted. Research challenges in railway asset management via application of big data analytics are identified and the future research directions are presented. INDEX TERMS Big data analytics, asset management, railway, data management, blockchain.

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Section: A Brief Discussion On Applications Of Big Data Analytics Fomentioning

confidence: 99%

Requirements for Big Data Adoption for Railway Asset Management

2020

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“…This is well-known within the computer science community, but is less known within the general engineering, and especially manufacturing research and practice communities. Recently, Aremu et al (2018) suggested what is essentially a tree-based organization of industrial databases for the purpose of data curation for condition monitoring. The authors propose a hierarchical organization of the industrial data based on a number of criteria, including the underlying equipment condition and behavior modes.…”

Section: Tree-structured Data Organizationmentioning

confidence: 99%

Physically Inspired Data Compression and Management for Industrial Data Analytics

Sabbagh

Cai

Stothert

et al. 2020

Front. Comput. Sci.

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With the huge and ever-growing volume of industrial data, an enormous challenge of how this data should be handled, stored, and analyzed emerges. In this paper, we describe a novel method that facilitates automated signal parsing into a set of exhaustive and mutually exclusive segments, which is coupled with extraction of physically interpretable signatures that characterize those segments. The resulting numerical signatures can be used to approximate a wide range of signals within some arbitrary accuracy, thus effectively turning the aforementioned signal parsing and signature extraction procedure into a signal compression process. This compression converts raw signals into physically plausible and interpretable features that can then be directly mined in order to extract useful information via anomaly detection and characterization, quality prediction, or process control. In addition, distance-based unsupervised clustering is utilized to organize the compressed data into a tree-structured database enabling rapid searches through the data and consequently facilitating efficient data mining. Application of the aforementioned methods to multiple large datasets of sensor readings collected from several advanced manufacturing plants showed the feasibility of physics-inspired compression of industrial data, as well as tremendous gains in terms of search speeds when compressed data were organized into a distance-based, tree-structured database.

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“…2 PdM is the monitoring of an asset or system's condition over its life cycle to provide a prognosis to when maintenance is required. 3,4 Along with the advent of more advanced sensors, collecting data has become a simple exercise, and an asset's life cycle data are composed of a lot of measurements that translate to tremendous amount of data. This has led to current PdM applications increasingly rely on data-driven Machine Learning (ML) algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…2 PdM is the monitoring of an asset or system’s condition over its life cycle to provide a prognosis to when maintenance is required. 3,4…”

Section: Introductionmentioning

confidence: 99%

An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study

Bekar

Nyqvist

Skoogh

2014

Advances in Mechanical Engineering

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Recent development in the predictive maintenance field has focused on incorporating artificial intelligence techniques in the monitoring and prognostics of machine health. The current predictive maintenance applications in manufacturing are now more dependent on data-driven Machine Learning algorithms requiring an intelligent and effective analysis of a large amount of historical and real-time data coming from multiple streams (sensors and computer systems) across multiple machines. Therefore, this article addresses issues of data pre-processing that have a significant impact on generalization performance of a Machine Learning algorithm. We present an intelligent approach using unsupervised Machine Learning techniques for data pre-processing and analysis in predictive maintenance to achieve qualified and structured data. We also demonstrate the applicability of the formulated approach by using an industrial case study in manufacturing. Data sets from the manufacturing industry are analyzed to identify data quality problems and detect interesting subsets for hidden information. With the approach formulated, it is possible to get the useful and diagnostic information in a systematic way about component/machine behavior as the basis for decision support and prognostic model development in predictive maintenance.

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Structuring Data for Intelligent Predictive Maintenance in Asset Management

Cited by 28 publications

References 21 publications

Requirements for Big Data Adoption for Railway Asset Management

Requirements for Big Data Adoption for Railway Asset Management

Physically Inspired Data Compression and Management for Industrial Data Analytics

An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study

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