PrimaVera: Synergising Predictive Maintenance

applied sciences Article PrimaVera: Synergising Predictive Maintenance Bram Ton 1, Rob Basten 2, John Bolte 3, Jan Braaksma 4, Alessandro Di Bucchianico 5, Philippe van de Calseyde 2, Frank Grooteman 6, Tom Heskes 7, Nils Jansen 7, Wouter Teeuw 1, Tiedo Tinga 8and Mariëlle Stoelinga 7,9,* 1Saxion University of Applied Sciences, 7513 AB Enschede, The Netherlands; [email protected] (B.T.); w[email protected] (W.T.) 2Department of Industrial Engineering & Innovation Sciences, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands; r[email protected] (R.B.); p.p.f.m.v[email protected] (P.v.d.C.) 3The Hague University of Applied Sciences, 2521 EN Den Haag, The Netherlands; [email protected] 4Department of Design, University of Twente, Production and Management, 7522 NB Enschede, The Netherlands; [email protected] 5Department of Mathematics and Computer Science, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands; [email protected] 6Royal Netherlands Aerospace Centre, 1059 CM Amsterdam, The Netherlands; [email protected] 7Institute for Computing and Information Sciences, Radboud University, 6525 XZ Nijmegen, The Netherlands; [email protected] (T.H.); [email protected] (N.J.) 8Department of Mechanics of Solids, Surfaces & Systems, University of Twente, 7522 NB Enschede, The Netherlands; [email protected] 9Formal Methods and Tools, University of Twente, 7522 NB Enschede, The Netherlands *Correspondence: [email protected] or [email protected] Received: 31 July 2020; Accepted: 19 November 2020; Published: 24 November 2020   Abstract: The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions. Keywords: predictive maintenance; process model; interdisciplinary research; case studies 1. Introduction Predictive maintenance is the ability to use data-driven analytics to optimise the upkeep of capital equipment [ 1 ]. Predictive maintenance bridges the gap between condition-based maintenance and corrective maintenance and is enabled by the advent of Industry 4.0 [ 2 ]. Value is created by transforming the collected data from intelligent systems into predictions about the system’s health, so that maintenance can be done exactly when and where needed. Estimates of the impact of predictive maintenance vary widely, but in general the return of investment is deemed to be favourable [ 3 ]. Despite the favourable return on investment, implementation of predictive maintenance in practice is still limited in many industries [4,5]. Furthermore, predictive maintenance is a key enabling technology for servitisation in smart industries. Servitisation is an emerging trend [ 6 ] in which organisations and citizens no longer own their assets, but rather lease their services: companies buy hours on production machineries with a Appl. Sci. 2020,10, 8348; doi:10.3390/app10238348 www.mdpi.com/journal/applsci Appl. Sci. 2020,10, 8348 2 of 19 guaranteed throughput; people lease a car rather than buying one. As a consequence, servitisation mandates constant availability at a low cost, prescriptive (personalised) service, and full digitisation and automation of service provision. Although the prospective benefits of predictive maintenance are tremendous, realising the envisioned benefits is far from trivial. While many core building blocks of predictive maintenance (such as sensor technology, failure prediction methods, and optimisation techniques) exist, current solutions focus on individual steps in the predictive maintenance cycle, and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks into an effective and efficient framework that supports optimal maintenance and asset management in a complex arena. The PrimaVera project picks up this challenge through a multidisciplinary team providing exactly these expertises. This paper highlights two major elements of the project to synergise predictive maintenance. First, the challenges hindering the successful application of predictive maintenance have been identified and are presented in this paper. Secondly, this paper introduces a generic predictive maintenance process model which provides a structured approach for deploying new predictive maintenance solutions. The PrimaVera project includes leading industrial partners from three major sectors of the Dutch economy: infrastructure, high-tech and maritime. The project has been awarded a grant of five million euros in funding from the Dutch Research Council (NWO) and co-funding from the participating consortium members. The project has a duration of five years. The rest of this paper is organised as follows. Section 2presents the state of the art of each of the six elements compromising predictive maintenance. Based on the state of the art, the PrimaVera project has identified four obstacles to overcome in order to utilise predictive maintenance to its full potential. These obstacles are described in Section 3, Section 4outlines the generic process model to tackle these obstacles. Furthermore, Section 4describes the research approach taken for each step of the process model. Section 5details the methodology used during the project together with a brief overview of the intended demonstrators. Section 6details the constituents of the consortium and finally, the last section contains the conclusion. 2. State of the Art Predictive maintenance entails six steps; data acquisition, data processing and diagnostics, prognostics, optimisation of maintenance and logistics, asset management, and human and organisational factors. This section will outline the state of the art of each step. Later on asset management and human and organisational factors are considered as one step as these two steps are closely related. 2.1. Data Acquisition Predictive maintenance or condition monitoring that goes beyond the visual inspections by a human inspector is always data-driven. The most rudimentary form of data-driven maintenance would be the analyses of log files and error messages [ 7 ]. A next level is using specific sensing methods, e.g., vibrational frequency measurements at bearings [ 8 ], to assess the health of components. More advanced methods include real-time monitoring which can raise an alarm based on predefined criteria. Taking it further, machine learning and big data analysis of sensor data are being researched [ 9 ]. In practice, many companies struggle to incorporate data-driven workflows within their company [ 4 , 5 ]. First of all, there may be no data, incomplete data, erroneous data, unaligned data or simply not enough data. Logging of errors may be incomplete or data are not stored at all. To apply the power of machine learning techniques to identify patterns, large amounts of data are needed, which means longer periods of time need to be measured. Secondly, data ownership is an issue. Even though machines log data, there is no access to the data, data are too costly or is owned by another company [ 10 ]. Thirdly, data are not labelled [ 10 ] or is labelled inconsistently. Operators use Appl. Sci. 2020,10, 8348 3 of 19 different nomenclature or report all malfunctions under the same generic error code. That makes it difficult to learn within a single company, let alone to learn from (the data of) ‘peer’ companies. What is needed is a structured approach to gather data in a goal-oriented way. The cross-industry standard process for data mining (CRISP-DM) [ 11 ] is a good starting point, but it describes common approaches. Therefore, specialisation towards specific predictive maintenance problems or domains may be valuable. Given an objective, knowing what to measure is a challenge, knowing how to measure is even more challenging. To overcome this challenge decision support tools which aid in the selection of an optimal sensor strategy can be used. For instance, optimal sensor placement can be found by genetic algorithms [ 12 ] or by using finite element models [ 13 ]. Sensor costs is another factor which can be optimised [ 14 ]. Other elements an optimal sensing strategy decision support tool should take into account are costs of asset/component replacement, expected faults and required accuracy of the condition monitoring system. Another factor the decision support tool should take into account is the goal of monitoring, for instance wear or fatigue. To our knowledge no research has been done into designing an overarching and unifying optimal sensing strategy decision support tool. Moreover, novel measuring technologies may appear which we are not even aware of. For instance, sensors on train axle boxes to enable the monitoring of insulated rail junctions [ 15 ]. Or mobile phones of commuters could be used, so-called participatory sensing [ 16 ]. For instance, mobile phones have been successfully used to monitor road conditions [ 17 ]. So new sensing technologies may show up for predictive maintenance as well. We see three main opportunities, i.e., challenges for data acquisition in predictive maintenance. First, the use of sensor fusion, in particular the combination of remote sensing (lidar, satellite, radar, sound-arrays, etc.) with in-situ sensors [ 18 ]. Sensor fusion may compensate for data gaps of a single sensor, leading to new sensing approaches by combining sensors at different distances. Second, the automatic context detection of sensor data. For instance, a bridge is supposed to expand during hot weather, but if the same expansion happens during cold weather this could indicate an anomaly. The challenge is to automatically detect the context (situational awareness). Third, methods for effective cross-company data interoperability are lacking [ 19 ]. These methods make it possible to enlarge data sets to get the size needed for machine learning. In particular this includes the definition of data quality: which quality of data is needed for which decision and how do we define this quality? 2.2. Data Processing and Diagnostics Automated data validation and correction for predictive maintenance requires methods that work under realistic assumptions. Within statistics and machine learning, many different techniques have been developed for dealing with missing data [ 20 – 22 ]. Most existing techniques rely on the missing completely at random (MCAR) assumption, which does not apply to the typical sensor data relevant for predictive maintenance. Recent approaches based on Gaussian copulas [ 23 , 24 ] can at least handle the missing at random (MAR) assumption, in which whether or not a data point is missing may depend on the values of other variables. A key challenge is to develop techniques that can further relax these assumptions and efficiently handle streaming big data, while at the same time identifying and correcting for outliers. Missing value imputation methods based on low-rank matrix completion such as [ 25 , 26 ] provide a good starting point: they are computationally efficient and their implicit projection of high-dimensional data into a lower-dimensional space naturally facilitates the robust detection of outliers [27,28]. Monitoring is an essential part of condition based maintenance, since monitoring the condition of systems allows the early identification of imminent failures. Current monitoring methods are not yet suitable for automated use, since they fail when there is no labelled training data, cannot handle high-dimensional data streams, do not adapt to data arriving at different time scales or do not take into account internal dependencies [ 29 ] and are not capable of making use of physical Appl. Sci. 2020,10, 8348 4 of 19 models. Regression-based monitoring methods have recently been extended to obtain adaptive detection thresholds in high-dimensional settings [ 30 ]. A first attempt to develop self-starting regression-based monitoring methods that do not required labelled training data has been presented in [ 31 ]. Purely statistical approaches have the advantage of providing performance guarantees, but they are difficult to automate. A promising recent approach to overcome this, is to use deep learning to correct for internal dependencies and use statistical approaches for monitoring [ 32 ]. The PrimaVera project will build upon these approaches by developing automated approaches with guaranteed performance that work in realistic industrial settings. In addition to these data-driven condition monitoring techniques, also more physics-based structural health monitoring techniques will be developed. These techniques typically utilise the dynamic response of systems and structures (e.g., vibrations) to detect and assess the presence, location and severity of damage [33]. To successfully design maintenance interventions, it is essential to understand why systems fail. The rapidly growing field of causal inference (see, e.g., the recent bestseller [ 34 ]) here may provide a solution. So-called transfer entropy [ 35 ] can be used to estimate the directed transfer of information between the time series of two variables, e.g., from sensors at different parts of a lithographic machine [ 36 ]. Causal discovery methods [ 37 , 38 ] aim to unravel the causal structure underlying the interactions between many different variables from purely observational data. From a methodological point of view, a key challenge is to integrate these two approaches to go from pairwise measures of causal information flow to a graphical structure that can be efficiently queried to find the root causes of specific failures. Whereas causal inference has been successfully applied in various scientific domains ( e.g., climate research [39] , neuroscience [ 40 ], proteomics [ 41 ], psychology [ 42 ]), its application in industrial settings is largely unprecedented. A methodological challenge here is to estimate transfer entropy in industrial settings. 2.3. Prognostics The aim of prognostics is to develop accurate algorithms to predict the future failures of components and systems. The prognostics step follows the data processing step and quantifies relevant key performance indicators (KPI), such as the remaining useful life (RUL), time to first failure, availability and reliability. Although a lot of research has already been done in this field, still several major challenges remain. The first challenge is the gap between component and system level. Most of the methods available in literature predict failures on a component level, e.g., for bearings [ 43 ], rail [ 44 ] or vehicle tracks [ 45 ]. However, asset owners are interested in the availability and expected failure of the complete system [ 46 ]. As developing separate models for all components in a system still takes too much time and effort, solutions have to be found in either predicting system level failures from only a limited number of (critical) component models, or in speeding up the component model development process. In the former case, the selection of these critical components, especially for large and complex systems, is not trivial and requires attention. The second challenge is that many predictive models heavily depend on a large and complete set of failure data. As for well-maintained critical systems failures are by definition rare, such data sets are often not available. This means that data-driven models must be combined with domain knowledge or physics-based prognostic methods [ 47 ]. This relates to the third challenge: only a small number of experts possess detailed knowledge on the failure behaviour of components, which is also very application-specific. This makes it difficult to incorporate that knowledge in generic prognostics tools. Automation of the failure or root cause analysis would make this knowledge more accessible. The fourth challenge in prognostics is that actual application of the methods proposed in scientific literature in industrial practice appears to be rather limited [ 48 ]. The main reason is that companies struggle to determine which approach fits with their ambition and their data and knowledge maturity. The final challenge is human factor related: engineers are typically reluctant to adopt advice or predictions from ‘black box’ prognostic tools. Especially fully data-driven and AI-based methods are hard to comprehend. Adding explainability [ 49 ] to these kind Appl. Sci. 2020,10, 8348 5 of 19 of methods might assist in increasing trust in the predictions. To summarise, prognostic methods are still considered to have high potential in predictive maintenance, but wide application in industry is still hindered by both technical and organisational challenges. 2.4. Maintenance and Logistics Optimisation The easiest way to plan maintenance is to perform it upon failure, i.e., perform corrective maintenance. However, this leads to many failures and high downtime. For decades now, most organisations have used some form of preventive maintenance: periodic maintenance. Maintenance is then triggered by, for example, running time, calendar time or number of take-offs of an aeroplane. The first models were proposed over 60 years ago by Barlow and Hunter [ 50 ]. Nowadays, predictive maintenance is an emerging trend. For predictive maintenance, information is used that results from data acquisition, data processing and diagnostics, and prognostics, such as RUL estimates or failure probabilities. Typically, as asset gets older, the RUL estimate goes down and the failure probability goes up. If these estimates would be perfect, maintenance could be performed exactly before breakdown. However, estimates are imperfect and an economic trade-off needs to be made. Performing preventive maintenance too early leads to unnecessary down-time. Performing preventive maintenance too late leads to corrective maintenance, which is typically much more costly since the maintenance has to be performed under high time pressure, leading to high logistics costs to get a service engineer with the right parts and tools at the asset. Furthermore, downtime of a critical component causes the complete asset to be down, which implies high downtime costs for its owner. This means that there is an economically optimal moment to perform maintenance that incorporates these costs and the probability of failure or RUL estimate. This optimisation is further complicated because assets contain many (critical) components, and grouping maintenance leads to fewer disruptions for the customer and lower logistics costs. Because it is of key importance to perform maintenance at the right time, there has been a lot of research on making optimal predictive maintenance decisions(for recent reviews, see, e.g., [5,51]). However, most of the research has been on single-item problems (i.e., one type of component). Exceptions, so papers focusing on multi-item problems, are those of Zhu [52] and Arts and Basten [53] . There has been some research on integrating maintenance and the service logistics needed to have the right parts, people, and tools available at the moment maintenance is planned [ 54 , 55 ] and on the usage of condition monitoring information to adapt operations [ 56 ]. Further integration of the topics of operations, maintenance, and service logistics is required. Another trending research area is making decisions with limited information. Since the prognostics and other information are often far from perfect, models and decision making need to take these imperfections into account. One way to do that is by modelling problems with partially observable Markov decision processes [ 57 ]. Such models are often hard to solve, but research on solving such models is ongoing [58]. 2.5. Asset Management and Organisational Factors Predictive maintenance is an active research area that has seen significant progress over the past decade, both in industry and in academia. Progress is much related to advancements in the area of big data analytics [ 59 ]. While many core building blocks of predictive maintenance (such as sensor technology, failure prediction methods, and optimisation techniques) exist, current solutions focus on individual steps in the predictive maintenance cycle and only work for very specific settings as discussed in the introductory chapter of this paper. Developing advanced maintenance techniques is therefore only useful if they are well integrated into an organisation [60]. A quote of a Maintenance Engineer at the Netherlands Railways who recently studied the use of predictive strategies illustrates these organisational challenges: “Preventive water filling based on real-time water level data and a predictive model seems to be an appropriate maintenance strategy; however, this requires the dynamic usage of human resources and Appl. Sci. 2020,10, 8348 6 of 19 filling stations . . . Trains move, making the logistic puzzle more complicated . . . Our overall goal is to maximise the availability of trains with functioning toilets in a cost-effective way.” Traditionally organisational aspects regarding the implementation of data-driven maintenance have been mentioned by other authors and have often been neglected [ 48 , 60 – 63 ]. Therefore, the PrimaVera project specifically studies the impact of data-driven maintenance on organisational processes where data-driven maintenance is being introduced. Procedures for the effective implementation of data-driven maintenance systems within organisations need to be designed in a timely way to allow effective use of its predictions in operational maintenance planning processes. Furthermore, earlier research shows that the implementation of predictive maintenance should include ambition levels, available data [ 64 ] and a fit of predictive maintenance with the organisational maturity of the organisation [ 60 ]. The following organisational interfaces have been identified by [ 60 ]: strategy and goals, decisions, structure, budget and capacity, and documentation. It can be debated that early integrated decision making is needed to evaluate the impact on these interfaces. Because asset management is a multi-disciplinary discipline, the organisational impacts expected by the introduction of data-driven maintenance systems on the aforementioned interfaces should therefore be approached from multiple perspectives. The perspectives mentioned by [ 65 ], e.g., technical, economic, commercial, compliance, and organisational aspects seem relevant to be used here, especially because asset management aspects are rarely limited to a one-dimensional perspective. The most critical organisational impacts should therefore ideally be identified and assessed before the introduction of data-driven maintenance by studying the use of the aforementioned perspectives in this specific asset management area. Because of the complexity of the associated systems, processes, and people there will always remain a number of organisational decisions that need to be identified and addressed before data-driven maintenance of individual components can actually be implemented. As [ 66 ] pointed out there are always trade-offs between maintenance costs, availability and efficiency in (multi-component) systems. It can be argued based on the outcomes of the work of Koochaki [ 66 ] that organisational processes need to become more flexible to make data-driven maintenance on a multi-component system more feasible. Therefore, the PrimaVera project will also investigate how organisational readiness and resilience in processes can be developed before or during the introduction of these systems. The use of high-reliability theory and anti-fragility in organisations can be seen as emerging fields [ 67 ] besides the needed attention for cultural aspects [60]. For the development of appropriate decision making support tools an iterative design science research (DSR) approach [ 68 ] is envisioned in which artefacts are iteratively evaluated and improved. A DSR strategy focuses on developing artefacts as well as knowledge creation, and aims to produce improvements based on a thorough understanding of problems or opportunities [ 68 ]. Therefore, the outcome of DSR is not only relevant to the practical application domain, but is also explicitly aimed at the creation of theoretical knowledge [69]. 2.6. Human Factors Human beings are critical to the functioning and performance of the majority of operating systems. However, human behaviour traditionally has been ignored in the field of operation management (OM). That is, most models in OM assume that agents who participate in operating processes are either fully rational or can be induced to behave rationally [ 70 , 71 ]. More specifically, these models assume that people have stable preferences, are not affected by cognitive biases or emotions, and have the ability to disregard irrelevant information by only responding to relevant information when making decisions [ 72 ]. The emerging field of Behavioural Operations Management departs from these (rather unrealistic) assumptions by acknowledging that human decision-makers are guided by emotions, cognitive biases or irrelevant situational cues that may affect the adoption and usage of operating systems [ 73 ]. More specifically, transforming maintenance systems and operations into ones that rely on data-driven technologies bring many challenges. One important challenge Appl. Sci. 2020,10, 8348 7 of 19 concerns the design of (data-driven) maintenance systems that organisational members are willing to trust and use [ 74 , 75 ]. That is, in order to successfully integrate these promising technologies into organisations, it is of critical importance to understand when and why users are hesitant to adopt these new technologies in their daily working routine and how we can stimulate its effective usage. As such, the goal and novelty of PrimaVera is to develop key insights into (i) what factors impacts a person’s acceptance and use of data-driven failure predictions and maintenance recommendations and (ii) how to effectively combine human judgement with the solution of a system. These insights will be used to design novel, user-centred maintenance tools that make the user–system interaction more effective and efficient. 3. Obstacles to Overcome To reap the fruits of predictive maintenance and leverage individual building blocks into an effective solution, the PrimaVera project has identified four cross-cutting obstacles that need to be overcome. These obstacles have been established based on our own expert interviews from people within academia and industry and are backed by recent insights from major consultancy firms [76,77]. The obstacles that thus far have hindered effective solutions are lack of orchestration, lack of automation, data uncertainty and the role of human and organisational factors. The justification of these obstacles is supported by an empirical Delphi-based scenario planning study conducted within the area of maintenance in digitalised manufacturing [ 78 ]. Each of these obstacles are detailed in the following sections. 3.1. Orchestration Current predictive maintenance solutions often focus on a single step in the predictive maintenance chain, with poor alignment to the rest of the workflow. This is suboptimal, since locally optimal solutions do not usually lead to overall optimal solutions. Thus, effective maintenance requires novel optimisation techniques that work across different aggregation levels. In particular, asset management involves supply chain optimisation, orchestrating the planning of maintenance personnel, equipment and groups of assets. Bokrantz et al. envision that effective maintenance will lead to optimised performance of entire manufacturing systems [ 78 ]. To achieve this the challenge is to develop methods and algorithms which are useful in practice [ 78 ]. Implementing predictive maintenance solutions which focus on a single failure mode are non-optimal, solutions must be implemented which consider the interaction of components with their operating environment [ 79 ]. Kipper et al. suggest that researchers need to develop studies to improve the understanding of how Industry 4.0 technologies and concepts impact processes, products and services [80]. 3.2. Automation Current applications of predictive maintenance usually consist of a large number of non-automated procedures. This is not only inefficient, but also error prone. Automating these steps into systematic procedures is challenging, because they involve a wealth of domain knowledge. In particular, accurate, scalable and robust algorithms for data cleaning, causal discovery of failures and root cause analysis are currently lacking, and the same holds for prediction algorithms for software and electronics, as well as for algorithms to optimise the supply chain logistics. Bokrantz et al. underline this obstacle by noting that developing maintenance management systems that automatically transform big data into decision support is still challenging [ 78 ]. Kipper et al. recommends future research should be carried out in order to develop frameworks for deploying Industry 4.0 in real applications, such as predictive maintenance, not only in large companies but also in Small Medium Enterprises (SMEs) [80]. Appl. Sci. 2020,10, 8348 8 of 19 3.3. Data Uncertainty Data from sensors or other sources is often inaccurate or incomplete. Obtaining accurate prognostics and maintenance decisions despite imperfect and uncertain data requires sophisticated methods that are capable of handling real world uncertainties [ 81 ]. Since uncertainties propagate along the predictive maintenance workflow, these techniques play a role in each step of the predictive maintenance cycle. Methods which evaluate the effectiveness and accuracy of predictive maintenance solutions with regards to uncertainty are required [81]. 3.4. Human and Organisational Factors The transition towards the Industry 4.0 requires organisations to embed the data-driven culture into their workflow. A key issue is the trust in data-driven decision support tools: maintenance decisions that are automatically computed by tools must be acted upon by maintenance engineers. This requires a user-centric design of these decision support tools. In addition, the project team responsible for successfully deploying a predictive maintenance solution is often confronted with reluctance and reservations [ 10 ]. The lack of communication between between theory developers and practitioners in the area of reliability and maintenance is also an issue [82]. Besides the right presentation of information, the right process information needs to be made available at the right moment to allow data-driven maintenance activities. Often there is limited information available on expected organisational impacts of data-driven maintenance actions. 4. Predictive Maintenance Process Model To overcome the before mentioned obstacles and to orchestrate the different steps in the predictive maintenance workflow a generic applicable process model is proposed to facilitate this (Figure 1). The proposed model is similar to existing models [ 83 – 87 ], which are in essence all based on the generic model of Jardine et al. [ 82 ]. The model of Jardine et al. has three distinct stages; data acquisition, data processing and maintenance decision-making. As the goal is to define a generically applicable process model for predictive maintenance, not only should it be applicable at system level but also at fleet level [ 88 , 89 ]. To ensure this genericity, the proposed model will also be based on the generic model of Jardine et al. To highlight the significance of diagnostics and prognostics within predictive maintenance, the proposed model explicitly breaks down the data processing step into these two elements. The importance of human and organisation factors is commonly overlooked by engineering disciplines [ 48 , 61 – 63 ] but is well rooted within information systems research [ 90 , 91 ]. Technology and behaviour are not dichotomous [ 90 ], therefore human and organisational factors have been added to the proposed model. This element is placed at the very centre of the model as it affects all other stages of the model. It is exactly this addition which sets the proposed model apart from the previously proposed models. The usability and applicability of the proposed model will be evaluated by applying it to the demonstrators described in Section 5.1. The generic model consists of five stages: (1) data are acquired from assets using sensors or other sources, (2) these data are then processed and turned into meaningful diagnostic information through data selection, cleaning and interpretation, (3) from this information predictions are made about the system’s health (prognostics), (4) based on these prognostics, maintenance and associated logistics are optimised, (5) all information has to be incorporated into a strategic asset management plan. Decisions listed in the asset management plan are transformed into actions which will affect the asset being managed, hence closing the cycle. An asset management plan documents the activities, resources and timescales required to achieve the organisation’s asset management objectives for an individual asset or group of assets [ 92 ]. Note that each of the stages relate to one or more of the before mentioned obstacles of predictive maintenance to overcome. The starting point within the predictive maintenance cycle depends on the motivation for asset management [ 93 ]. This motivation can be initiated by a technology push; existing technology is Appl. Sci. 2020,10, 8348 9 of 19 available which needs to be management, in this case the cycle would start with data acquisition. On the other hand motivation can be initiated by a decision pull; there is a certain economic necessity, in this case the cycle would start with an asset management plan. Human and organisational factors Assets Data Information Performance indicators Asset management plan Data acquisition Data processing and diagnosis Prognostics Maintenance and logistics optimisation Decisions Figure 1. Predictive maintenance management process model. 4.1. PrimaVera Approach The subsequent sections will focus on each individual stage of the proposed process model and will describe the envisaged scientific outcome for this stage. Once again it should be stressed that the project’s main endeavour is to enrol a holistic, cross-sectoral approach, thus explicitly addressing the observed obstacle of a lack of orchestration. 4.1.1. Data Acquisition In practical settings selection of suitable sensors for implementing a predictive maintenance solution pose a challenge [ 10 ]. To overcome this, a decision support tool will be realised that advises on the most appropriate sensing techniques, spatial sensor placement and optimal sensing strategy to monitor an asset. Though there is a lot of work on optimal sensor placement [ 12 , 13 , 94 ], an overarching decision support tool which takes all facets of predictive maintenance into account is still lacking. Input to such a decision support tool will include critical components together with their failure modes, required accuracy and resolution, cost factors and expert domain knowledge. Implementing an optimal sensing strategy will aid in the mitigation of data uncertainty. One of the case studies being analysed in the PrimaVera project is a sludge dredger’s propulsion system. A limited amount of recorded failure data is available of this system, only eight clearly labelled eminent failures are present. In order to obtain accurate prognostic models, more failure data are required. Therefore, various methods to acquire more failure data will be evaluated. First method is a model based approach to gather more failure data. 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