A Taxonomy of Recurring Data Analysis Problems in Maintenance Analytics

Modern maintenance strategies increasingly focus on vast amounts of diverse data and multifaceted analytical approaches in order to make efficient use of given resources and unveil hidden potentials. While there is often no universal solution approach to a specific case at hand, it is still possible to observe recurring problem classes for which generic solution templates can be applied and thus the establishment of a reusable knowledge base appears beneficial. To this end, we apply a taxonomy development approach to identify and systematize dimensions and characteristics of recurring data analysis problems in data-driven maintenance scenarios. Our research method integrates findings from a systematic literature review and expert interviews with data scientists from industry. Thus, we add descriptive theory to the field of maintenance analytics and propose a taxonomy that distinguishes between analytical maintenance objectives, data characteristics and analytical techniques

Maintenance management of tractors and agricultural machinery: Preventive maintenance systems

Agricultural machinery maintenance has a crucial role for successful agricultural production.  It aims at guaranteeing the safety of operations and availability of machines and related equipment for cultivation operation.  Moreover, it is one major cost for agriculture operations.  Thus, the increased competition in agricultural production demands maintenance improvement, aiming at the reduction of maintenance expenditures while keeping the safety of operations.  This issue is addressed by the methodology presented in this paper.  So, the aim of this paper was to give brief introduction to various preventive maintenance systems specially condition-based maintenance (CBM) techniques, selection of condition monitoring techniques and understanding of condition monitoring (CM) intervals, advancement in CBM, standardization of CBM system, CBM approach on agricultural machinery, advantages and disadvantages of CBM.  The first step of the methodology consists of concept condition monitoring approach for the equipment preventive maintenance; its purpose is the identification of state-of-the-art in the CM of agricultural machinery, describing the different maintenance strategies, CM techniques and methods.  The second step builds the signal processing procedure for extracting information relevant to targeted failure modes.   Keywords: agricultural machinery, fault detection, fault diagnosis, signal processing, maintenance managemen

Establishment of a novel predictive reliability assessment strategy for ship machinery

There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme.There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme

Setting sail towards predictive maintenance:developing tools to conquer difficulties in the implementation of maintenance analytics

Unexpected downtime of equipment is disruptive in complex manufacturing supply chains and imposes high costs due to forgone productivity. Executives in asset-intensive industries therefore regard such unexpected failures of their physical assets as a primary operational risks to their business. Predictive maintenance (PdM) (including condition-based maintenance) can aid practitioners in preventing these unexpected failures and getting insight into current and future behaviour of their assets. However, the use of PdM in practice seems to lag behind recent technological advancements and our theoretical understanding. The current study therefore aims to further develop our understanding on the use and adoption of predictive maintenance and, based on these observations, develop tools to better support the practical application of predictive maintenance. This research is guided by the following research question: How can the practical application of predictive maintenance better be supported? To be able to answer this question, an explorative multiple-case study is conducted including fourteen cases from various industries in the Netherlands to study successful applications of predictive maintenance. The focus in this multiple-case study lays on both the technical and the organizational aspects of PdM, because the organizational application process of PdM seems overlooked by the academic literature. The multiple case study reveals that almost all organizations who applied PdM successfully have followed a costly trial and error process. This appears to be the result of the technical and organizational complexity of the application of PdM and the absence of effective theoretical guidance in: (i) selecting the most suitable techniques for PdM; (ii) identifying the most suitable candidates for PdM; and (iii) evaluating the added value of PdM. To conquer the three main identified problems and to assist practitioners in the implementation of PdM, three corresponding decision support tools – which can be used together – have been designed in the remainder of this dissertation. The three solutions are designed using a structured design science process. Therefore, after studying the problems in-depth to define design criteria and select design principles, the developed solutions are demonstrated in practice using case studies in various industries. Future research should be guided towards the refinement and testing of the provided methods

Architecting Integrated System Health Management for Airworthiness

Integrated System Health Management (ISHM) for Unmanned Aerial Systems (UAS) has been a new area of research - seeking to provide situational awareness to mission and maintenance operations, and for improved decision-making with increased self-autonomy. This research effort developed an analytic architecture and an associated discrete-event simulation using Arena to investigate the potential benefits of ISHM implementation onboard an UAS. The objective of this research is two-fold: firstly, to achieve continued airworthiness by investigating the potential extension of UAS expected lifetime through ISHM implementation, and secondly, to reduce life cycle costs by implementing a Condition-Based Maintenance (CBM) policy with better failure predictions made possible with ISHM. Through a series of design experiments, it was shown that ISHM presented the most cost-effective improvement over baseline systems in situations where the reliability of the UAS is poor (relative to manned systems) and the baseline sensor exhibited poor qualities in terms of missed detection and false alarm rates. From the simulation results of the test scenarios, it was observed that failure occurrence rates, sensor quality characteristics and ISHM performance specifications were significant factors in determining the output responses of the model. The desired outcome of this research seeks to provide potential designers with top-level performance specifications of an ISHM system based on specified airworthiness and maintenance requirements for the envisaged ISHM-enabled UAS

An embedded distributed tool for transportation systems health assessment

International audienceThis article presents an embedded distributed tool for health assessment of complex systems. The presented architecture is based on a solving method for embedded technical diagnostics and prognostics. This tool provides services enabling the evaluation of the health status of complex systems. Diagnostic services provide information for the maintenance decision support system that leads to reduce the periods of unavailability and determine if their future mission can be carried out. The diagnostic and prognostic functions are detailed and the exchanged data are specified. An example shows the feasibility of the proposed architecture and demonstrates the correctness of the developed algorithms

Aircraft electrical power system diagnostics, prognostics and health management

In recent years, the loads needing electrical power in military aircraft and civil jet keep increasing, this put huge pressure on the electrical power system (EPS). As EPS becomes more powerful and complex, its reliability and maintenance becomes difficult problems to designers, manufacturers and customers. To improve the mission reliability and reduce life cycle cost, the EPS needs health management. This thesis developed a set of generic health management methods for the EPS, which can monitor system status; diagnose faults/failures in component level correctly and predict impending faults/failures exactly and predict remaining useful life of critical components precisely. The writer compared a few diagnostic and prognostic approaches in detail, and then found suitable ones for EPS. Then the major components and key parameters needed to be monitored are obtained, after function hazard analysis and failure modes effects analysis of EPS. A diagnostic process is applied to EPS using Dynamic Case-based Reasoning approach, whilst hybrid prognostic methods are suggested to the system. After that, Diagnostic, Prognostic and Health Management architecture of EPS is built up in system level based on diagnostic and prognostic process. Finally, qualitative evaluations of DPHM explain given. This research is an extension of group design project (GDP) work, the GDP report is arranged in the Appendix A

Analysis of Artificial Intelligence based diagnostic methods for satellites

The growing utilization of small satellites in various applications has emphasized the need for reliable diagnostic methods to ensure their optimal performance and longevity. This master thesis focuses on the analysis of artificial intelligence-based diagnostic methods for these particular space assets. This work firstly explores the main characteristics and applications of small satellites, highlighting the critical subsystems and components that play a vital role in their proper functioning. The key components of this study revolve around Diagnosis, Prognosis, and Health Monitoring (DPHM) systems and techniques for small satellites. The DPHM systems aim at monitoring the health status of the satellite, detecting anomalies and predicting future system behavior. The reason why advanced DPHM systems are of interest for the space operators is the fact that they mitigate the risk of satellites catastrophic failures that may lead to service interruptions or mission abort. To achieve these objectives, a hybrid architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed. This architecture leverages the strengths of CNNs in feature extraction and LSTM networks in capturing temporal dependencies. The integration of these two neural network architectures enhances the diagnostic capabilities and enables accurate predictions for small satellite systems. Real data collected from an operational satellite is utilized to validate and test the proposed CNN-LSTM hybrid architecture. Based on the experimental results obtained, advantages and drawbacks of the exploitation of this architecture are discussed.The growing utilization of small satellites in various applications has emphasized the need for reliable diagnostic methods to ensure their optimal performance and longevity. This master thesis focuses on the analysis of artificial intelligence-based diagnostic methods for these particular space assets. This work firstly explores the main characteristics and applications of small satellites, highlighting the critical subsystems and components that play a vital role in their proper functioning. The key components of this study revolve around Diagnosis, Prognosis, and Health Monitoring (DPHM) systems and techniques for small satellites. The DPHM systems aim at monitoring the health status of the satellite, detecting anomalies and predicting future system behavior. The reason why advanced DPHM systems are of interest for the space operators is the fact that they mitigate the risk of satellites catastrophic failures that may lead to service interruptions or mission abort. To achieve these objectives, a hybrid architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed. This architecture leverages the strengths of CNNs in feature extraction and LSTM networks in capturing temporal dependencies. The integration of these two neural network architectures enhances the diagnostic capabilities and enables accurate predictions for small satellite systems. Real data collected from an operational satellite is utilized to validate and test the proposed CNN-LSTM hybrid architecture. Based on the experimental results obtained, advantages and drawbacks of the exploitation of this architecture are discussed

Reducing Uncertainty in PHM by Accounting for Human Factors - A Case Study in the Biopharmaceutical Industry

The ultimate goal of prognostics within Through-life Engineering Services (TES) is to accurately predict the remaining useful life (RUL) of components. Prognostic frameworks inherently presume that there is predictability in the failure rate of the system, i.e. a system experiencing exclusively stochastic failure events cannot, by definition, be predictable. Prediction model uncertainties must be bound in some logical way. Therefore, to achieve an accurate prognostic model, uncertainty must first be reduced through the identification and elimination of the root causes of random failure events. This research investigates human error in maintenance activities as a major cause of random failure events, using a case study from the biopharmaceutical industry. Elastomer failures remain the number one contamination risk in this industry and data shows unexplained variability in the lifetime of real components when compared to accelerated lifetime testing in the lab environment. Technician error during installation and maintenance activities of elastomers is one possible cause for this and this research explores how these errors can be eliminated, reduced, or accounted for within the reliability modeling process. The initial approach followed was to improve technician training in order to reduce errors and thereby reduce the variability of random failure events. Subsequent data has shown an improvement in key metrics with failures now more closely matching data from lab testing. However, there is scope for further improvements and future research will explore the role of performance influencing factors in the maintenance task to identify additional causes of variation. These factors may then be incorporated as a process variable in a prognostics and health management (PHM) model developed for the system. The paper will present these data fusion approaches accounting for human factors as a roadmap to improving PHM model reliability

Vehicle level health assessment through integrated operational scalable prognostic reasoners

Today’s aircraft are very complex in design and need constant monitoring of the systems to establish the overall health status. Integrated Vehicle Health Management (IVHM) is a major component in a new future asset management paradigm where a conscious effort is made to shift asset maintenance from a scheduled based approach to a more proactive and predictive approach. Its goal is to maximize asset operational availability while minimising downtime and the logistics footprint through monitoring deterioration of component conditions. IVHM involves data processing which comprehensively consists of capturing data related to assets, monitoring parameters, assessing current or future health conditions through prognostics and diagnostics engine and providing recommended maintenance actions. The data driven prognostics methods usually use a large amount of data to learn the degradation pattern (nominal model) and predict the future health. Usually the data which is run-to-failure used are accelerated data produced in lab environments, which is hardly the case in real life. Therefore, the nominal model is far from the present condition of the vehicle, hence the predictions will not be very accurate. The prediction model will try to follow the nominal models which mean more errors in the prediction, this is a major drawback of the data driven techniques. This research primarily presents the two novel techniques of adaptive data driven prognostics to capture the vehicle operational scalability degradation. Secondary the degradation information has been used as a Health index and in the Vehicle Level Reasoning System (VLRS). Novel VLRS are also presented in this research study. The research described here proposes a condition adaptive prognostics reasoning along with VLRS