Big data analytics for predictive maintenance in maintenance management

Razali, Muhammad Najib; Jamaluddin, Ain Farhana; Jalil, Rohaya Abdul; Nguyen, Thi Kim

doi:10.1108/pm-12-2019-0070

Cited by 11 publications

(7 citation statements)

References 22 publications

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“…In [85][86][87][88][89][90][91][92]129], data-driven approaches, including big data analytics relating to data from various sources, including sensor data, historical records, external factors, and data mining, have been used to improve the accuracy and comprehensiveness of PdM systems. Significantly, interactive and intuitive data visualization tools have contributed to quickly understanding the equipment's health and making informed decisions.…”

Section: State-of-the-art Techniques For Predictive Maintenancementioning

confidence: 99%

“…In contrast, the time and costs of gathering and cleaning data may be considerable. In addition, it is essential to have large amounts of data for learning deep learning models, and in certain cases, they may be challenging to obtain [85,86,129,192,210]. AI-based PdM systems should be tested and validated under various conditions, such as different types of equipment, different operating conditions, and different levels of data quality for successful real-world applications.…”

Section: Challenges and Limitations Of Using Ai For Pdm Autonomymentioning

confidence: 99%

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Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Ucar,

Karakose,

Kırımça

2024

Applied Sciences

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Predictive maintenance (PdM) is a policy applying data and analytics to predict when one of the components in a real system has been destroyed, and some anomalies appear so that maintenance can be performed before a breakdown takes place. Using cutting-edge technologies like data analytics and artificial intelligence (AI) enhances the performance and accuracy of predictive maintenance systems and increases their autonomy and adaptability in complex and dynamic working environments. This paper reviews the recent developments in AI-based PdM, focusing on key components, trustworthiness, and future trends. The state-of-the-art (SOTA) techniques, challenges, and opportunities associated with AI-based PdM are first analyzed. The integration of AI technologies into PdM in real-world applications, the human–robot interaction, the ethical issues emerging from using AI, and the testing and validation abilities of the developed policies are later discussed. This study exhibits the potential working areas for future research, such as digital twin, metaverse, generative AI, collaborative robots (cobots), blockchain technology, trustworthy AI, and Industrial Internet of Things (IIoT), utilizing a comprehensive survey of the current SOTA techniques, opportunities, and challenges allied with AI-based PdM.

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Section: State-of-the-art Techniques For Predictive Maintenancementioning

confidence: 99%

Section: Challenges and Limitations Of Using Ai For Pdm Autonomymentioning

confidence: 99%

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Ucar,

Karakose,

Kırımça

2024

Applied Sciences

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“…Smart technologies are widely applied in all stages of construction production as shown in Table 2. They include (1) design automation and legal review using BIM and AI in the design stage [9][10][11][12][13][14], (2) real-time monitoring of construction resources using IoT and drones and construction automation through intelligent construction equipment and robots in the construction stage [15][16][17][18][19][20][21][22][23], and (3) monitoring and operation optimization using sensing and big data analytics in the operation stage [24][25][26][27][28][29]. According to a scientometric review of smart construction sites in construction engineering and management by Liu et al [30], the focus gradually shifted from traditional project performance-related concerns, such as hybrid information and performance evaluation, to practical applications of smart technologies in worker and construction site-associated areas.…”

Section: Smart Technology In Construction Projectsmentioning

confidence: 99%

Performance-Influencing Factors and Causal Relationships of Construction Projects Using Smart Technology

Kim,

Lim,

Lee

et al. 2023

Buildings

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With the advent of the Fourth Industrial Revolution, construction technology innovation through high-tech convergence is actively taking place, and the smart construction technology market is growing rapidly. However, as it focuses on the use of individual technologies, research into the factors that have a major impact on their intended effect is insufficient. Thus, this study investigates these factors and their mutual influences from various perspectives to promote the use of smart technology to improve construction projects. Ten performance-influencing factors were derived from four perspectives based on the balanced scorecard technique. In addition, based on a survey of projects that use smart technology, the current status of its application and performance characteristics were analyzed, and a causal relationship model among the factors was presented. This study provides a foundation for identifying major areas for the efficient use of smart technology and performance measurement, and it will contribute to the introduction and activation of smart construction technology.

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“…When an unpredictable fault occurs in a building, a maintenance process starts entailing a corrective action. In large and distributed buildings organizations, such as hospitals, universities and other public administrations, the number of contemporary corrective actions can be relevant, even if preventive maintenance approaches are in use (Dzulkifli et al, 2021;Ferreira et al, 2021;Hong et al, 2015;Rampini and Cecconi, 2022;Razali et al, 2020;Shalabi and Turkan, 2020). In these contexts, the maintenance process is usually managed by facility management (FM) contractors.…”

Section: Introductionmentioning

confidence: 99%

Influence of pre-processing methods on the automatic priority prediction of native-language end-users’ maintenance requests through machine learning methods

D’Orazio,

Bernardini,

Di Giuseppe

2024

ITcon

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Feedback and requests by occupants are relevant sources of data to improve building management, and building maintenance. Indeed, most predictable faults can be directly identified by occupants and communicated to facility managers through communications written in the end-users’ native language. In this sense, natural language processing methods can support the request identification and attribution process if they are robust enough to extract useful information from these unstructured textual sources. Machine learning (ML) can support assessing and managing these data, especially in the case of many simultaneous communications. In this field, the application of pre-processing and ML methods to English-written databases has been widely provided, while efforts in other native languages are still limited, impacting the real applicability. Moreover, the performance of combinations of methods for pre-processing, ML and classification classes attribution, has been limitedly investigated while comparing different languages. To fill this gap, this work hence explores the performance of automatic priority assignment of maintenance end-users’ requests depending on the combined influence of: (a) different natural language pre-processing methods, (b) several supervised ML algorithms, (c) two priority classification rules (2-class versus 4-class), (d) the database language (i.e. the original database written in Italian, the native end-users’ language; a translated database version in English, as standard reference). Analyses are performed on a database of about 12000 maintenance requests written in Italian concerning a stock of 23 buildings open to the public. A random sample of the sentences is supervised and labelled by 20 expert annotators following the best-worst method to attribute a priority score. Labelled sentences are then pre-processed using four different approaches to progressively reduce the number of unique words (potential predictors). Five different consolidated ML methods are applied, and comparisons involve accuracy, precision, recall and F1-score for each combination of pre-processing action, ML method and the number of priority classes. Results show that, within each ML algorithm, different pre-processing methods limitedly impact the final accuracy and average F1-score. In both Italian and English conditions, the best performance is obtained by NN, LR, SVM methods, while NB generally fails, and by considering the 2-class priority classification scale. In this sense, results confirm that facility managers can be effectively supported by ML methods for preliminary priority assessments in building maintenance processes, even when the requests database is written in end-users’ native language.

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Big data analytics for predictive maintenance in maintenance management

Cited by 11 publications

References 22 publications

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Performance-Influencing Factors and Causal Relationships of Construction Projects Using Smart Technology

Influence of pre-processing methods on the automatic priority prediction of native-language end-users’ maintenance requests through machine learning methods

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