Proactive Buildings: A Prescriptive Maintenance Approach

Prescriptive maintenance has recently attracted a lot of scientific attention. It integrates the advantages of descriptive and predictive analytics to automate the process of detecting non nominal device functionality. Implementing such proactive measures in home or industrial settings may improve equipment dependability and minimize operational expenses. There are several techniques for prescriptive maintenance in diverse use cases, but none elaborates on a general methodology that permits successful prescriptive analysis for small size industrial or residential settings. This study reports on prescriptive analytics, while assessing recent research efforts on multi-domain prescriptive maintenance. Given the existing state of the art, the main contribution of this work is to propose a broad framework for prescriptive maintenance that may be interpreted as a high-level approach for enabling proactive buildings

Prescriptive maintenance

Tema ovog rada je analiza procesa preskriptivnog održavanja i primjer njegove implementacije. U prvom poglavlju navedene su postojeće strategije održavanja i njihove karakteristike, zatim je u drugom opisana preskriptivna analitika i njena važnost kod preskriptivnog održavanja. U trećem poglavlju detaljno je prikazan proces preskriptivnog održavanja koji se sastoji od prikupljanja velike količine podataka, njihove analize i predviđanja kvarova pomoću metoda strojnog učenja, propisivanja aktivnosti s kojima se nastanak kvara može spriječiti ili odgoditi te konačne impementacije aktivnosti održavanja i analize uspješnosti. Zadnje poglavlje sadrži primjer implementacije u elektroenergetskoj tvrtki Solas i primjer preskriptivnog održavanja na oblaku.The topic of this paper is an analysis of prescriptive maintenance and its application in industry. The first chapter contains existing maintenance strategies along with characteristics of each one. Then in the second chapter there is a brief explanation of prescriptive analytics along with its role in prescriptive maintenance. Third chapter contains a detailed process of prescriptive maintenance, which consist of gathering Big Data, analysis of the data and fault prediction using machine learning methods, prescripting a maintenance activity to avoid or prolong failure occurrence and the assessment of the current state maturity. In the final section of this paper, example of implementation of prescriptive maintenance is given along with prescriptive maintenance on cloud

Sociotechnical Implementation of Prescriptive Maintenance for Onshore Wind Turbines

Electricity generated by wind turbines (WT) is a mainstay of the transition to renewable energy. In order to economically utilize WT is, operating and maintenance costs, which account for 25% of total electricity generation costs in onshore WT’s, are a focus of cost reduction activities. Implementing a data-driven prescriptive maintenance approach is one way to achieve this. So far, various approaches for prescriptive maintenance for onshore WT’s have been suggested. However, little research has addressed the practical implementation considering sociotechnical aspects. The aim of this paper is therefore to identify success factors for the successful implementation of such a maintenance strategy with clear and holistic guidance on how existing knowledge on prescriptive maintenance from science can be transferred to business practice. These recommendations are developed through case study research and classified in the four structural areas of Acatech’s Industry 4.0 Maturity Index: Resources, Information Systems, Organizational Structure and Culture

A Prescriptive Maintenance Aligned Production Planning and Control Reference Process

Digital innovations can improve various business processes, such as production planning and control (PPC). In the last years, prescriptive maintenance (PxM) emerged as a strategy to increase overall production performance, but an alignment of the PPC process with PxM has not been examined yet. To tackle this problem, a PxM-aligned PPC process is designed and evaluated in this study using a reference model development methodology, including a narrative literature review, a multivocal literature review, and eight expert interviews. The reference model shows where process elements benefit from PxM alignment, how alignment can be achieved from a process and output, data, function, and organization view, and where fits and gaps between theory and practice are

An Analysis of Barriers Preventing the Widespread Adoption of Predictive and Prescriptive Maintenance in Aviation

The aviation industry has long recognized the potential benefits of predictive maintenance, a maintenance strategy that leverages sensor and operational data to predict the future degradation of components. Prescriptive maintenance takes this a step further and considers the entire aviation ecosystem to schedule maintenance actions optimally. With the ability to reduce maintenance costs by up to 30%, as reported by the Department of Energy, these maintenance strategies have been identified to be an important investment to reduce a airline costs. However, despite great interest and technological advances in areas such as diagnostics, prognostics, sensing, computation, and machine learning, the adoption of predictive and prescriptive maintenance has not been widely applied in aviation. To shed light on this issue, we conducted an analysis of the barriers preventing or limiting the adoption of predictive and prescriptive maintenance in aviation. Through discussions with subject matter experts across industry, academia, standards bodies, and government, we identified five key challenges: complexity of prediction; validation, safety assurance, and regulatory challenges; cost of adoption; difficulty in quantifying impact and informing decisions; and data availability, quality, and ownership challenges. This study provides a detailed overview of these barriers and areas where stakeholders could invest to overcome them, aiming to support the scaled adoption of predictive and prescriptive maintenance in aviation

Improving Aircraft Maintenance Performance through Prescriptive Maintenance Strategies

In the past couple of years, predictive maintenance has arguably been the most discussed maintenance strategy in academia and industry. While it promises a significant operational easing and cost saving potential through the projection of system failures, it is characterized by a strong asset centricity; thus, it often only focuses on a system’s (projected) condition for issuing a maintenance task. Furthermore, the realized savings potential depends heavily on the performance of the underlying condition monitoring technologies. A recent study by (Haarman et al. 2018) among manufacturing companies from the Netherlands, Belgium, and Germany showed that many of these companies struggle in the identification of viable business case scenarios with predictive maintenance; mostly due to the limited maturity of the underlying condition monitoring technologies. Subsequently, this lack of business cases increases their hesitance to (further) invest in the development of the associated technologies, slowing the technological advancement unnecessarily. An integral part in the development of post-prognostics maintenance strategies is the identification of suitable systems to apply a prognostics-based maintenance strategy to and the determination of necessary minimum performance criteria of the underlying monitoring technology. However, the majority of research publications and industry efforts focusses on the development of condition-monitoring techniques itself and often oversimplifies this identification of business case scenarios and the subsequent integration of derived maintenance actions within the existing maintenance process environment. With these challenges in mind, we propose the next step in the evolution of post-prognostics maintenance strategies – the prescriptive maintenance approach. With this step, the scope of maintenance scheduling will be extended beyond the asset itself and incorporate the associated stakeholder’s objectives in the planning process, e.g. for the operator, a reduction in flight irregularities or, for the maintenance provider, a reduction in unjustified component removals (Wheeler et al. 2010). Thus, individual improvements (or possible drawbacks) – due to different maintenance strategies – can be attributed to the respective stakeholder. With this presentation, we will demonstrate the expected benefits for an automated tire condition monitoring system using our discrete-event simulation framework PreMaDe (Prescriptive Maintenance Developer). In particular, we are going to focus on the effects that different Prognostics and Health Management (PHM) technologies have on the operations of a short-/medium-haul aircraft fleet, the associated on-wing maintenance, and the spare parts inventory management. The presented results will provide a holistic view on the expected maintenance performance and not solely focus on monetary aspects – since real-life decision always require a trade-off between competing objectives or among multiple stakeholders. This will, subsequently, help maintenance practitioners to define suitable business case scenarios and determine necessary payments for stakeholders to be financially compensated for adversarial effects of such a prognostics-based maintenance strategy. Ultimately, this approach will enable the swift identification of systems that promise a significant optimization potential through the introduction of an adjusted maintenance strateg

Evaluation of Prescriptive Maintenance Strategies for a Tire Pressure Indication System (TPIS) assuming Imperfect Maintenance

Digital technologies for condition monitoring enable failure predictions of aircraft systems and will have an enormous impact on aircraft maintenance in the future. Current research studies evaluate the potential of these technologies within prescriptive maintenance strategies. These strategies combine failure predictions of a system with given operational environmental conditions to identify the optimal time for maintenance. However, current prescriptive maintenance models neglect the impact of imperfect maintenance, which affects system reliability, system availability and its associated costs. The goal of this thesis is to investigate how this impact of imperfect maintenance affects prescriptive maintenance strategies including the tire pressure indication system. In addition, an aviation maintenance stakeholder model for a prescriptive simulation model of the tire pressure indication system is presented, which will allow for more precise strategy development in future implementations. This aviation maintenance stakeholder model includes the stakeholders aircraft maintenance, airline, mechanics, logistics service providers, and component maintenance. The imperfect maintenance model considers the impact of human factors in addition to technological influences. The effects of fatigue, procedure design, certifications, training, experience, age, and environmental conditions are included into the model to determine the degree of imperfection of a performed task. Imperfect maintenance considers discretely performed maintenance tasks and calculates a new system state after the execution of an imperfect repair. For the development of prescriptive maintenance strategies for the tire pressure indication system, the obtained results of a sensitivity analysis are used. In this context, the parameters of the imperfect maintenance model are analyzed with regard to the objective maintenance cost. These strategies are subsequently compared to the defined state-of-the-art tire maintenance process. In comparison, the application of the tire pressure indication system in addition to human factor optimization indicates the highest cost savings potential of 67 percent

Condition Monitoring of Rotary Machinery Using Industrial IOT Framework: Step to Smart Maintenance

Modern maintenance strategies, such as predictive and prescriptive maintenance, which derived from the concept of Industry and Maintenance 4.0, involve the application of the Industrial Internet of Things (IIoT) to connect maintenance objects enabling data collection and analysis that can help make better decisions on maintenance activities. Data collection is the initial step and the foundation of any modern Predictive or Prescriptive maintenance strategy because it collects data that can then be analysed to provide useful information about the state of maintenance objects. Condition monitoring of rotary equipment is one of the most popular maintenance methods because it can distinguish machine state between multiple fault types. The topic of this paper is the presentation of an automated system for data collection, processing and interpretation of rotary equipment state that is based on IIoT framework consisting of an IIoT accelerometer, edge and fog devices, web API and database. Additionally, ISO 10816-1 guidance has been followed to develop module for evaluation of vibration severity. The collected data is also visualized in a dashboard in a near-real time and shown to maintenance engineering, which is crucial for pattern monitoring. The developed system was launched in laboratory conditions using rotating equipment failure simulator to test the logic of data collection and processing. A proposed system has shown that it is capable of automated periodic data collection and processing from remote places which is achieved using Node RED programming environment and MQTT communication protocol that enables reliable, lightweight, and secure data transmission