Systematic Literature Review Predictive Maintenance Solutions for SMEs from the Last Decade
Abstract
:1. Introduction
- Has fewer than 250 employees
- Its annual turnover is less than EUR 50 million or its annual balance sheet is less than EUR 43 million total.
2. Research Methodology
2.1. Scope and Objective
2.2. Research Questions (RQs)
- Q1.
- What are the most significant expectations and concerns of stakeholders in SMEs in regard to implementing PdM?
- Q2.
- What are the most important challenges SMEs are faced with regarding predictive maintenance?
- Q3.
- What equipment, facilities, and resources are required for PdM techniques in small- and medium-sized companies?
- Q4.
- How have recent studies provided an appropriate solution to implement predictive maintenance by considering the previous questions?
2.3. Search Strategy and Database
- IEEE Xplore Digital Library (www.ieeexplore.ieee.org, accessed on 28 April 2021)
- Springer (www.springerlink.com, accessed on 28 April 2021)
3. Early Results of SLR and RQs
3.1. Article Extraction Process
3.2. Stakeholder Expectations
3.3. SME Challenges
3.4. Requirement
3.5. Summary of Studies and RQ Answers
- Data collection: To obtain relevant data and manage its content. Data can be collected from a variety of sources, including sensors, RFID tags, people, and so on.
- Data transfer: The collected data need to be transferred without affecting their content. Data are transferred from the source to the data management system.
- Data integration: Combining data from different sources in a data warehouse using methods that ensure its quality.
- Data analysis: Data analysis and extract information and knowledge to support decision making by managers.
- Visualization: By visualizing the information required by the users or decision makers. Visualization can be statistical or reporting.
4. Discussion
5. Conclusions
6. Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Categories | Data Source | Search String | Specific Filter |
---|---|---|---|
Group A | IEEE | All Metadata: “Predictive Maintenance” in command search tab (under advanced search) | Date between 2010 and 2020 |
Springer Link | |||
Group B | IEEE | All Metadata: “Predictive Maintenance” AND (“Machine Learning”) in command search tab (under advanced search) | |
Springer Link | |||
Group C | IEEE | All Metadata: Predictive Maintenance AND All Metadata: small enterprisesORAll Metadata: Predictive Maintenance AND All Metadata: SMEsORAll Metadata: Predictive Maintenance AND All Metadata: small companies | |
Springer Link | ‘“Predictive Maintenance” AND (“small enterprises” OR “SMEs” OR “small companies”)’ in command search tab (under advanced search) |
Data Source | Group A | Group B | Group C |
---|---|---|---|
IEEE | 2666 | 522 | 20 |
Springer Link | 2524 | 992 | 176 |
Actors | Interactions between the entities and stakeholders |
Adopt a philosophy in an integrated organization | |
Adopt a strategy in an integrated organization | |
Budget reclassification between units | |
Information | Acquire the required qualifications |
Value Proposition | Requires a systematic process for service innovation by digital technology |
Implement a culture of failure in the organization |
Solutions | Expectation | Requirement | Challenge |
---|---|---|---|
Deep belief network (DBN) method to predict backlash error [17] | 🗶 | ||
Predictive virtual enterprise maintenance processes [18] | 🗶 | ||
Proposed single-board computer (the Raspberry Pi 3 Model B) and IIOT device (the Sense HAT) [20] | 🗶 | ||
User-friendly interface and integrated platform (FGS2I4.0) [24] | 🗶 | 🗶 | |
A proposed toolkit for the implementation of Industry 4.0 [9] | 🗶 | ||
Suggestion of the axiomatic design for the implementation of a specific solution for each enterprise [9] | 🗶 | ||
Cloud computing and IOT solutions [22] | 🗶 | 🗶 | |
Logistic regression and random forest (RF) for the design of predictive models in the Industry 4.0 environment [38] | 🗶 | 🗶 | |
Support of the top management, external knowledge, and the usage of benchmarks [21] | 🗶 | 🗶 | |
Wireless technologies and mobile systems [25] | 🗶 | 🗶 | 🗶 |
Combination prediction method of power transformers based on the grey model [26] | 🗶 | 🗶 | 🗶 |
Instant email notification system for every maintenance schedule generated [27] | 🗶 | ||
Human-level concept learning and hierarchical probabilistic learning [35] | 🗶 | ||
Titan software platform for integrating production environments withIndustrial DevOp [45] | 🗶 | ||
Localization of knowledge [37] | 🗶 |
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Hassankhani Dolatabadi, S.; Budinska, I. Systematic Literature Review Predictive Maintenance Solutions for SMEs from the Last Decade. Machines 2021, 9, 191. https://doi.org/10.3390/machines9090191
Hassankhani Dolatabadi S, Budinska I. Systematic Literature Review Predictive Maintenance Solutions for SMEs from the Last Decade. Machines. 2021; 9(9):191. https://doi.org/10.3390/machines9090191
Chicago/Turabian StyleHassankhani Dolatabadi, Sepideh, and Ivana Budinska. 2021. "Systematic Literature Review Predictive Maintenance Solutions for SMEs from the Last Decade" Machines 9, no. 9: 191. https://doi.org/10.3390/machines9090191