Investigation of degradation and upgradation models for flexible unit systems: a systematic literature review

future internet Review Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review Thirupathi Samala 1, Vijaya Kumar Manupati 1, Maria Leonilde R. Varela 2,* and Goran Putnik 2   Citation: Samala, T.; Manupati, V.K.; Varela, M.L.R.; Putnik, G. Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review. Future Internet 2021,13, 57. https://doi.org/10.3390/fi13030057 Academic Editor: Stefano Rinaldi Received: 24 January 2021 Accepted: 19 February 2021 Published: 25 February 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Department of Mechanical Engineering, NIT Warangal, Warangal 506004, India; [email protected] (T.S.); [email protected] (V.K.M.) 2Department of Production and Systems, School of Engineering, University of Minho, 4804-533 Guimarães, Portugal; [email protected] *Correspondence: [email protected] Abstract: Research on flexible unit systems (FUS) with the context of descriptive, predictive, and prescriptive analysis have remarkably progressed in recent times, being now reinforced in the current Industry 4.0 era with the increased focus on integration of distributed and digitalized systems. In the existing literature, most of the work focused on the individual contributions of the above mentioned three analyses. Moreover, the current literature is unclear with respect to the integration of degradation and upgradation models for FUS. In this paper, a systematic literature review on degradation, residual life distribution, workload adjustment strategy, upgradation, and predictive maintenance as major performance measures to investigate the performance of the FUS has been considered. In order to identify the key issues and research gaps in the existing literature, the 59 most relevant papers from 2009 to 2020 have been sorted and analyzed. Finally, we identify promising research opportunities that could expand the scope and depth of FUS. Keywords: flexible unit systems; degradation; residual life distribution; workload strategy; upgradation; predictive maintenance 1. Introduction Recently, the manufacturing systems domain underwent a paradigm shift by introducing several key enabling technologies as a requirement of Industry 4.0 [ 1 ]. Keeping in mind clients’ customized requirements and global manufacturers’ personalized production, the current production and process capabilities need to be transformed. For example, recent requirements such as shorter product life cycles, high production rates, jobs complexity, quality products, and cost effectiveness are the most significant factors for any manufacturing industry [ 2 ]. Considering all the foregoing requirements, and, in addition, according with the current market demand and society requests, there is a need to enhance the system’s capabilities by maintaining it under control from system breakdowns and several external forces that have not been considered as a highest priority in the past decade. To accomplish these challenges, there is a need for high machine availability, flexibility, configurability, and accessibility of manufacturing processes, as mentioned in [ 3 – 9 ]), along with another interesting contribution for emphasizing the necessity of increasing the level of flexibility of manufacturing systems, which can be seen in https://publications.muet.edu.pk/index.php/muetrj (accessed on 23 January 2021). However, various manufacturing systems available to fulfil the above-mentioned requirements have costs affairs and high maintenance. In this review paper, we introduced a special kind of configuration: i.e., flexible unit systems (FUS) with one degree of flexibility, two degrees of flexibility, semi flexibility, and highly flexible configurations, where the reconfiguration and upgradation of unit (machine) systems are easily achieved [10,11]. The common factors from different studies that affect FUS are identified as degradation rate, residual life distribution, workload strategy, upgradation, and predictive maintenance. Future Internet 2021,13, 57. https://doi.org/10.3390/fi13030057 https://www.mdpi.com/journal/futureinternet Future Internet 2021,13, 57 2 of 17 To improve the health status of the system and to make the manufacturing functions effective and efficient, system-level health monitoring is new thinking to which nowadays researchers are paying attention. Therefore, the degradation rate at the system level is of the highest priority. Studies have shown that manufacturing systems are subjected to degradation both with age and usage, including wear, cracking, and fatigue, among others; whereas the residual life of a machine was characterized as remaining useful till its level of degradation arrives at a predefined failure threshold [ 12 ]. Real-time production data from complex systems produce a huge variety and volume of data. Handling this kind of dataintensive system with conventional statistical tools may be insufficient when firms seek to strategically conceal the data [ 13 ]. Hence, there is a need for advanced analytics such as descriptive, predictive, and prescriptive analytics to analyze the machine’s historical data to improve the efficiency of the system by knowing the health condition at every stage. Given this scenario, towards summarizing the status of present research and to stimulate the future investigations, the main aim of this paper is to carry out a Systematic Literature Review (SLR) with respect to the degradation and upgradation models for FUS. Hence, a review of manufacturing systems in the context of three analytics has been considered, particularly with flexibility as a key common word. The analysis of the reviewed literature enabled us to develop a comprehensive conceptualization as shown in (Figure 1). It is the conceptualization that was used to classify the findings and it was also referenced for future research. Future Internet 2021, 13, x FOR PEER REVIEW 2 of 18 The common factors from different studies that affect FUS are identified as degradation rate, residual life distribution, workload strategy, upgradation, and predictive maintenance. To improve the health status of the system and to make the manufacturing functions effective and efficient, system-level health monitoring is new thinking to which nowadays researchers are paying attention. Therefore, the degradation rate at the system level is of the highest priority. Studies have shown that manufacturing systems are subjected to degradation both with age and usage, including wear, cracking, and fatigue, among others; whereas the residual life of a machine was characterized as remaining useful till its level of degradation arrives at a predefined failure threshold [12]. Real-time production data from complex systems produce a huge variety and volume of data. Handling this kind of data-intensive system with conventional statistical tools may be insufficient when firms seek to strategically conceal the data [13]. Hence, there is a need for advanced analytics such as descriptive, predictive, and prescriptive analytics to analyze the machine’s historical data to improve the efficiency of the system by knowing the health condition at every stage. Given this scenario, towards summarizing the status of present research and to stimulate the future investigations, the main aim of this paper is to carry out a Systematic Literature Review (SLR) with respect to the degradation and upgradation models for FUS. Hence, a review of manufacturing systems in the context of three analytics has been considered, particularly with flexibility as a key common word. The analysis of the reviewed literature enabled us to develop a comprehensive conceptualization as shown in (Figure 1). It is the conceptualization that was used to classify the findings and it was also referenced for future research. Figure 1. Framework addressing the topics affecting flexible unit systems (FUS). Manufacturing systems Residual Life Distribution Workload strategy Throughput rate Prognostics and health management for unit systems Flexible unit systems Diagnostics for unit systems Degradation Resource Management Descriptive and predictive model management Operations Management Upgradation Prescriptive model Management Data Management Include Include Include Include Figure 1. Framework addressing the topics affecting flexible unit systems (FUS). The paper is structured as follows. In Section 2, a detailed research methodology is used, which follows SLR’s five-step approach. Effectiveness of degradation and upgradation models on the FUS and findings have been presented in Section 3. Discussion and Future Internet 2021,13, 57 3 of 17 Future research agenda is explained in Section 4. Conclusions and future work directions are pointed out in Section 5. 2. Research Methodology This research followed the SLR as a basic scientific activity that delivers a clear and comprehensive overview compared to descriptive literature reviews. The formation of a basic framework for an in-depth analysis and a scientific process can be possible by using this SLR. The systematic literature followed a sequence of five steps, as mentioned in [ 10 ], which are as follows. (1) Formation of questions; (2) Finding the studies; (3) Study preference and evaluation; (4) Investigation and combination; (5) Reporting and using the results. Step 1. Formation of questions: Research Question 1. What is the role of degradation, residual life distribution, workload strategy, upgradation, and predictive maintenance on flexible unit systems? Research Question 2. How to integrate the degradation and upgradation models to the flexible unit systems? Step 2. Finding the studies: This step concerns how to find and choose the bibliographic database or search engine, and additionally the search strings. The research questions have been considered in this search for literature reviews. Following similar literature reviews [ 14 – 16 ] and three bibliographic databases, i.e., Web of Science, Scopus, and Science Direct, a remarkable quantity of published literature on degradation rate, residual life distribution, workload strategy, upgradation, and predictive maintenance, including very relevant and important journals in this area, has been considered. Additionally, also considered were advanced analytics, like descriptive, predictive, and prescriptive ones, to analyze the machine’s historical data for improving the efficiency of the system. Tables 1–3show the search strings searched in the data bases and the results obtained using the three mentioned databases. However, sorting the selected research articles and selecting the publication title between 2009–2020 shows 603 articles for the search string “Flexible unit systems” (or) “Flexible machine systems” and “Degradation” (or) “Degradation rate”, 167 articles for the search string “Flexible unit systems” (or) “Flexible machine systems” and “Residual Life Distribution” (or) “Residual life”, 140 articles for the search string “Flexible unit systems” (or) “Flexible machine systems” and “workload strategy” (or) “workload adjustment”, 104 articles for the search string “Flexible unit systems” (or) “Flexible machine systems” and “Upgradation”, and 243 articles for the search string “Flexible unit systems” (or) “Flexible machine systems” and “Predictive Maintenance”, respectively. Future Internet 2021,13, 57 4 of 17 Table 1. Search string and number of results from Web of Science. Search String Search Field Date of Search No. of Results “Flexible unit systems” (or) “Flexible machine systems” and “Degradation” (or) “Degradation Rate” Topic 11 August 2020 273 “Flexible unit systems” (or) “Flexible machine systems” and “Residual Life” (or) “Residual Life Distribution” Topic 11 August 2020 34 “Flexible unit systems” (or) “Flexible machine systems” and “Workload strategy” (or) “Workload adjustment” Topic 11 August 2020 42 “Flexible unit systems” (or) “Flexible machine systems” and “Upgradation” Topic 11 August 2020 2 “Flexible unit systems” (or) “Flexible machine systems” and “Predictive Maintenance” Topic 11 August 2020 41 Table 2. Search string and number of results from Scopus. Search String Search Field Date of Search No. of Results “Flexible unit systems” (or) “Flexible machine systems” and “Degradation” (or) “Degradation Rate” Article title, abstract, keywords 4 September 2020 178 “Flexible unit systems” (or) “Flexible machine systems” and “Residual life” (or) “Residual life Distribution” Article title, abstract, keywords 4 September 2020 9 “Flexible unit systems” (or) “Flexible machine systems” and “Workload strategy” (or) “Workload adjustment” Article title, abstract, keywords 4 September 2020 14 “Flexible unit systems” (or) “Flexible machine systems” and “Upgradation” Article title, abstract, keywords 4 September 2020 1 “Flexible unit systems” (or) “Flexible machine systems” and “Predictive Maintenance” Article title, abstract, keywords 4 September 2020 9 Table 3. Search string and Number of Results from Science direct. Search String Date of Search No. of Results “Flexible unit systems” (or) “Flexible machine systems” and “Degradation” (or) “Degradation Rate” 18 September 2020 152 “Flexible unit systems” (or) “Flexible machine systems” and “Residual life” (or) “Residual life Distribution” 18 September 2020 124 “Flexible unit systems” (or) “Flexible machine systems” and “Workload strategy” (or) “Workload adjustment” 18 September 2020 84 “Flexible unit systems” (or) “Flexible machine systems” and “Upgradation” 18 September 2020 101 “Flexible unit systems” (or) “Flexible machine systems” and “Predictive Maintenance” 18 September 2020 193 Step 3. Study preference and Evaluation: In this step, filtering criteria were explicated, to choose only relevant studies to add in the review, in which the studies actually addressed the research questions. From 1995 to 2008, articles were excluded because they were just consigned to the small percentage of the examples. 11 years (2009–2020) of related studies were performed to focus on recent studies, methodologies, and technologies. The article journals of document type were sorted from the search results and the best articles distributed in peer-reviewed journals in English were contemplated. Colicchia et al. [ 17 ] argue that restricting the search to Future Internet 2021,13, 57 5 of 17 peer-reviewed journals enables one to reach better results due to the rigorous reviewing processes inherent to such articles before their publication. This exercise reduces the number of journal articles to 198. After checking the duplicates (initially in each search string and after, taking into consideration all search strings set together), titles and abstracts of the selected journal articles were analyzed for relevance, which enabled us to further reduce the number of articles to 106. Articles qualified for review had to fulfil the five major criteria: (i) articles related to finding the Degradation level of manufacturing systems, (ii) articles related to finding the residual life of manufacturing systems, (iii) articles related to adjustment strategy of workload to reduce the degradation level of manufacturing systems, (iv) articles related to upgradation of manufacturing systems, and (v) articles focused on predictive maintenance of manufacturing systems. At this step, the number of articles for investigation was 106. At last, a more examined analysis of the 66 articles was made with the full gratified review. Step 4. Investigation and Combination: In this step, the content of each paper was analyzed to identify the key issues. Through full-content review, different articles were excluded, which were not as per the specified research focus of this study. In this way, the number of definite articles for the investigation was reduced to 59, as recorded in Table 4. Table 4. Summary of articles preferences and evaluation. Bibliographic Database Analysis Search 1 Search 2 Search 3 Search 4 Search 5 Total Web of Sciences 273 34 42 2 41 392 Scopus 178 9 14 1 9 211 Science Direct 152 124 84 101 193 654 Inclusion/Exclusion criteria of Web of Sciences Date Range 193 29 26 1 28 277 Document Type 191 29 26 1 28 275 Research Area 175 26 23 1 26 251 Language 174 26 22 1 26 249 Inclusion/Exclusion criteria of Scopus Date Range 155 9 11 1 6 182 Document Type 130 6 7 1 6 150 Research Area 109 6 6 1 6 128 Language 96 6 6 1 6 115 After checking the duplicates (in each search) 113 22 36 3 24 198 After checking the duplicates (in all search) 106 Analysis of (Abstract and Title) 66 After a detailed article analysis 59 Step 5. Reporting and using the results: The data contained in 59 articles were summarized, then prepared with connected categories, for example, methodologies used in their research and various key findings. Table 5shows the list of journals related to the number of articles published as well as the year of publication. Reliability Engineering and Systems Safety,International Journal of Advanced Manufacturing Technology,IIE Transactions on Automation Science and Engineering, Journal of Intelligent Manufacturing,IFAC online,CIRP Annals: Manufacturing Technology, and IEEE Transactions on Reliability contributed to 55% of the total articles published Future Internet 2021,13, 57 6 of 17 related to factors (degradation, residual life distribution, workload strategy, upgradation, and predictive maintenance) related to manufacturing systems. Other journals like the Journal of Computers & Industrial Engineering,IEEE Transactions,Journal of Manufacturing Systems,Procedia Manufacturing,European Journal of Operations Research, and a few other journals contributed to 45% of the total journal articles published related to factors affecting manufacturing systems. Table 5. List of journals related to the parameters related to the flexible unit systems. Sl. No. Name of the Journal Number of Articles Year of Publishing 1 Reliability Engineering and Systems Safety 5 2012,14,17,19 2IEEE Transactions on Automation Science and Engineering 4 2015,16 3International Journal Advanced Manufacturing Technology 3 2015,18 4 IEEE Transactions on Reliability 3 2014,15,17 5 CIRP Annals: Manufacturing Technology 3 2017,19 6 Journal of Intelligent Manufacturing 3 2009,2014 7 IFAC online 3 2017,19 8 Journal of Manufacturing Systems 2 2018 9 International Journal of Production Research 2 2015,17 10 IIE Transactions 2 2014,15 11 Procedia Manufacturing 2 2017 12 Computers & Industrial Engineering 2 2017,19 13 IEEE Transactions on Power Systems 2 2015 14 IEEE Systems Journal 2 2019 15 European Journal of Operation Research 2 2018 16 Journal of Precision Engineering and Manufacturing Technology 1 2009 17 Materials Today: Proceedings 1 2018 18 International Journal of Productivity and Quality Management 1 2016 3. Findings The relevant data were collected and studies arranged dependent on five factors, mentioned in the research methodology. The detailed description of these five factors and their relevance under study is as follows. 3.1. Prognostics and Health Management (PHM) for Unit Systems In recent years, PHM has emerged as an essential approach in the global competitive market, achieving advantages over others by improving system maintainability and reliability. However, the application of PHM to flexible unit systems is a challenging task as systems are more complex. Specifically, small and medium-sized ventures experienced difficulty in applying PHM, because of the lack of resources and time for research and development. Shin et al. [ 18 ] explored how the Prognostics method is an intelligent answer for enhancing the availability of unit systems and fault prognosis to evaluate residual life. A PHM model for manufacturing systems integrated with different online sensors with different flexible structures has been developed by [ 19 ], and Hao et al. [ 12 ] proposed a contemporary sign partition as well as prognostics structure for multi-section systems with non-resolute segment signals, and Fang et al. [ 20 ] developed a prognostic procedure that uses multi-stream signals for predicting the residual life of partially degraded manufacturing systems. Future Internet 2021,13, 57 7 of 17 3.1.1. Throughput Rate The throughput rate is significant for the design and the activity of manufacturing systems. A remarkable quantity of throughput rate related research has been developed to estimate the throughput of manufacturing systems by creating analytical methods with various unreliable machines. Hao et al. [ 12 ] characterized the “throughput rate” of a manufacturing system, which is equivalent to summing up all the workloads from each unit. Table 6shows the literature related to degradation of manufacturing systems. In FUS, this performance measure is considered one of the important expected outputs due to its direct relevance for capacity. For example, if a FUS consists of three different machines with different capacities, then the maximum throughput is considered as the summation of all three machines. If the expected demand is less than the capacity of the system, the throughput rate is equal to the demand, otherwise the throughput rate is equal to the total capacity. Table 6. Literature review on degradation rate related to flexible unit systems. Literature Review on Degradation Rate in the Context of Flexible Unit Systems Sl. No. References Findings 1 [21]The machine’s degradation was analyzed in view of an impact on machine performance and product quality utilized as the performance index. 2 [22] A new degradation model, “Transformed Inverse Gaussian process”, has been presented in this paper. 3 [23]Shows that it can be conceivable to make robust reconfigurable manufacturing systems by taking the degradation of modules. 4 [12] The multistage manufacturing measures have been utilized to focus on modelling the interconnection between product quality degradation and tool wear. 5 [24]Addresses the issues of maintenance, joint production, for an untrustworthy production system subjected to degradation. 6 [22]Researches Inverse Gaussian models for degradation investigation, with constant monotonic degradation rates also mentioned. 7 [25] Introduces a degradation modelling system for assessing and updating the RLDs of partially degraded segments using an FPT approach. 8 [26]Works on the availability of machines as well as random failure rate to fulfil economically a random demand under certain constraints. 9 [27] Describes linear-quadratic stochastic production planning issues so as to fulfil a random demand. 3.1.2. Degradation Degradation is a stochastic process, which will occur through random shocks and also through the components being worn in manufacturing processes. Degradation rate plays a significant role in the life of FUS because the impact of the degradation process on different types of manufacturing systems are observed on the failure severity. A Degraded machine impacts on the nature of the parts manufactured where the defectives rely upon the production rate, as has been mentioned in [ 28 ]. Zied et al. [ 27 ] worked on the degradation of the unit as stated by the rate of production. Hajej et al. [ 26 ] explained that their examination is to investigate the impact of the production rate on the degradation level and machine availability. Through this diverse literature, it was shown that the degradation process was grouped in two ways, i.e., continuous degradation and discrete degradation. Zhenggeng et al. [ 21 ] explained about multiple degradation methods, which involve continuous degradation, as well as that discrete degradations have been modelled through various stochastic processes, for example, Markov renewal and gamma processes. Zhang et al. [ 29 ] proposed that the conventional Wiener process-dependent degradation is an important degradation model technique for manufacturing systems. With this, the past research on the degradation of manufacturing systems showed that efforts have been made to characterize the relation between degradation rate and workload adjustment strategy by using a Bayesian approach to find the residual life distribution literature, as is mentioned below in Table 7. Future Internet 2021,13, 57 8 of 17 Table 7. Literature review on residual life distribution related to flexible unit systems. Literature Review on Residual Life Distribution in the Context of Flexible Unit Systems Sl. No. References Findings 1 [30]To predict the Residual Life under time differing conditions, the degradation rate changing and unexpected signal bounds at condition change points have been proposed. 2 [29] In this paper, an attempt was made to audit and sum up the ongoing demonstrating improvements of Wiener process models for assessing the Residual life. 3 [31]A data-driven technique for Residual life expectation depends on a Bayesian approach that has been proposed. 4 [32]In this paper, remaining useful life prediction of slightly degraded parts with co-dependent degradation processes have been shown. 5 [33]Describes the fundamental steps needed to execute the Prognostics and Health Management System, so that the remaining useful life of CNC milling cutters can be predicted. 3.1.3. Residual Life Distribution A machine’s or a component’s residual life estimation during its operation based on its present condition is very important in order to find its health condition. Li et al. [ 30 ] proposed a remaining useful life prediction by introducing the degradation rate changing to transition function, and it jumps the degradation signals towards the measurement function. For example, in the manufacturing industry, the usage of a prognostic health management system for deciding the residual life of a milling cutter in a high-speed milling machine depends on externally measured conditions, as has been mentioned in [33]. Bian et al. [ 32 ] introduced how prediction of the life of a complex manufacturing system needs an exact estimation of degradation conditions of its constituent parts as well as an adequate understanding of how these stages progress in the future. Si et al. [ 34 ] proposed s degradation method to anticipate the remaining useful life of machines utilizing a recursive channel calculation. Zhang et al. [ 29 ] surveyed modelling improvements of the Wiener process strategies for degradation information examination, remaining useful life estimation as their implementation in the empirics of the health management of manufacturing systems. Mosallam et al. [ 31 ] presented two stages of an information-driven strategy for remaining useful life prediction. It is noted that based on the residual life of a manufacturing unit, a workload adjustment strategy will be helpful to maintain the production rate mentioned in Hao et al. [ 12 ]. The various literature related to workload strategy has been mentioned below in Table 8. Table 8. Literature review on workload strategy related to flexible unit systems. Literature Review on Workload Strategy in the Context of Flexible Unit Systems Sl. No References Findings 1 [35]Investigates the effects of various workload strategy methodologies on manufacturing system performance by a mathematical study. 2 [36]A workload adjustment has been proposed to find the extreme workload to the remaining working units to fulfil the manufacturing prerequisites. 3 [37]Focuses on the dynamic workload adjustment to manage the degradation of all the units in a compound system. 4 [38] Works on dynamic workload adjustment strategy to control the degradation of units. 3.1.4. Workload Strategy A dynamic workload adjustment technique has been proposed by [ 36 ] to locate the most extreme workload machinery. In their work, the highest degraded machines were identified to satisfy the production necessities on parallel configurations. With various benchmark instances, simulation tests have been conducted to assess the degradation rate. Li et al. [ 35 ] explored the effects of various workload adjustment methodologies on Future Internet 2021,13, 57 9 of 17 a system agent-based simulation approach. To prevent the overlap of machine failure within a period of time, Hao et al. [ 39 ] developed a method to control the degradation and predicted failure time of each machine by adjusting the workload. Similarly, the allocation of buffer capacity is especially important in order to obtain an acceptable throughput and work-in-progress, as mentioned in [40]. 3.1.5. Descriptive and Predictive Model Management The arrangement of the present smart manufacturing systems is subjected to the capacity for (a) sensibly modelling the production system, (b) predictable plant information, (c) solving issues proficiently with computational attempts, and (d) including feedback to raise the decision-making on top of time. Hence, enabling descriptive and predictive analytics for the estimation of manufacturing systems performance is a greater concern in the current information and digital age. 3.1.6. Resource Management Resource management is the way towards planning, scheduling, and allocating resources in the best possible way. More observation is on future manufacturing, where resource management is a greater concern and it must handle more proficiently. Particularly different manufacturing, as well as automotive industries, are advancing towards utilization of resources to improve proficiency and profitability without trading off the current manufacturing capacity. De Ryck et al. [ 41 ] proposed a methodology that makes resource management in automated guided vehicle systems more effective. The resource management aims at providing robust strategies in manufacturing systems to accomplish the resource allocation and to solve related issues, for example, resource levelling, and production layout adjustment in production planning. 3.2. Diagnostics for Unit Systems Present manufacturing systems are outfitted with different sensors that provide continuous checking and diagnosis, but sensors cannot be equipped across all the parts in the manufacturing system due to big data challenges. These outcomes in non-observable parts limit our capacity to help successful and continuous real-time monitoring and fault diagnosis activities. The exact diagnosis is the most significant step because the fault is the primary cause of a manufacturing system’s failure in the fault treatment. Among a wide range of possible faults in a manufacturing system, operative faults occur most often (about 70%). Djelloul et al. [ 42 ] solved maintenance optimization issues in manufacturing systems by considering the diagnosis and suggested a hybrid neural network technique focusing on developing a diagnosis system. Qin et al. [ 43 ] proposed that a fault identification, as well as a diagnostic module, is depicted dependent on an internal programmable logical controller. Generally, manufacturing industries have a large number of machines with different old programmable logic controllers that can benefit from an upgrade to new technology. The literature related to the upgradation of manufacturing equipment is mentioned below in Table 9. Table 9. Literature review on upgradation related to flexible unit systems. Literature Review on Upgradation in the Context of Flexible Machine Systems Sl. No. References Findings 1 [44]Introduces a plan for usage of a data preparing kit that will upgrade a manufacturing machine allowing it to coordinate into an industry 4.0 environment. 2 [45] Explains that the traditional manufacturing industry upgrading is partially important in this trend. 3 [46] Explores the situation of a system upgrade, both electronics and mechanical, which requires extensive software modifications. 4 [47] Considers the problems of selecting and upgrading equipment for creating and upgrading production systems on facilities with discrete manufacturing. Future Internet 2021,13, 57 16 of 17 34. Si, X.-S.; Wang, W.; Hu, C.-H.; Chen, M.-Y.; Zhou, D.-H. A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation. Mech. Syst. Signal Process. 2013,35, 219–237. [CrossRef] 35. Li, H.; Parlikad, A. Study of dynamic workload assignment strategies on production performance. IFAC PapersOnLine 2017 ,50, 13710–13715. 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