For nearly two decades, business models such as Functional Products have been in focus within research and of interest in the manufacturing industry. Functional product offers consist of hardware, software, service -support systems and management of operation which, when developed in an integrated manner, together provide the customer with an agreed-upon function with a specified level of availability. Compared to product-oriented sales, this type of business model can provide added value to customers, usually through an increase in the service content. Due to the total care commitment, offering Functional Products requires management of reliability and maintainability in order to meet the availability requirement of the function provided. The development of the Functional Product must include holistic analysis and prediction of the functional product availability performance to reduce technical and economic risks and ensure that the function is delivered according to contract. The research performed in this thesis presents an integrated development approach for monitoring and simulation to predict functional product availability. It is shown how the constituents of a functional product can be modelled in an integrated manner in order to simulate and predict functional product availability. A part of this modelling strategy is demonstrated through a simulation case example to show that is possible through this approach to evaluate the availability of different functional product designs. To support the development of the monitoring capability needed for availability simulations it is shown how it is possible to develop fault detection and diagnosis methods for fault detection systems based on data stream management systems. It is also shown how data stream forecasting can be used to predict failures due to faults occurring at short notice. Different fault detection methods have been developed, tested and evaluated on real industrial applications to verify applicability as queries on data streams, managed by data stream management systems. The results from these tests have been evaluated for their predictive performance and detection accuracy. Finally, methodological and technological approaches to monitoring and analysis in functional product development and similar business models to functional products are reviewed. The results showed that few research contributions address the information perspective in functional product development and similar business models holistically. The integrated development approach presented is a pragmatic approach to functional product development which is based on the merged research results of the papers included and knowledge domain presented.

The field of fault detection and diagnosis has been the subject of considerable interest in industry. Fault detection may increase the availability of products, thereby improving their quality. Fault detection and diagnosis methods can be classified in three categories: data-driven, analytically based, and knowledge-based methods. In this work, we investigated the ability and the performance of applying two fault detection methods to query data streams produced from hydraulic drive systems. A knowledge-based method was compared to a data-driven method. A fault detection system based on a data stream management system (DSMS) was developed in order to test and compare the two methods using data from real hydraulic drive systems. The knowledge-based method was based on causal models (fault trees), and principal component analysis (PCA) was used to build the data-driven model. The performance of the methods in terms of accuracy and speed, was examined using normal and physically simulated fault data. The results show that both methods generate queries fast enough to query the data streams online, with a similar level of fault detection accuracy. The industrial applications of both methods include monitoring of individual industrial mechanical systems as well as fleets of such systems. One can conclude that both methods may be used to increase industrial system availability

A functional product is an integrated package consisting of hardware, software and a service support system that provides a customer with a certain function and is sold under a performance-based contract that includes a functional availability guarantee. For the availability performance, prediction, optimisation and management of risk are therefore important concerns during product development. This paper describes a software tool that can generate an integrated model of a functional product from its design details and analyse it through simulation to provide availability performance information. The model's application to the analysis of a real industrial system is demonstrated. Such tools are important for the development and widespread adoption of functional products. The resulting analysis gave an indication of a suitable guaranteed functional availability level for the product and could be used to compare the performance of different design options.

Today, competition among product manufacturers has increased and many facebeing copied and thus losing their uniqueness. To differentiate on the market,new businesses, which extend the manufacturer’s responsibility throughsupporting and guaranteeing product performance, thus offering more value-creating activities, have received increasing interest.The concepts of Functional Products or Total Offers have emerged through the realization that it is tougher to compete with hardware products only. The Functional Products concept combines the development of hardware, software, services and management of operation, thereby integrating value-adding activities into complete offers. Offering a Functional Product is usually a lifecycle commitment (long-term business-to-business agreement) between the function provider and customer. In this type of business, agreed-upon availability is of importance, especially since the availability of a function can directly influence customers’ productivity. The provider and customer need to predict and evaluate the availability of the function, both in early phases of the product development process and continuously during operation throughout the contracted time. In this thesis, in paper A, a model for predicting and monitoring industrial system availability has been proposed to enable the provider of Functional Products to continuously evaluate, through simulation, the offer in terms of availability performance. The model integrates hardware, support system and monitoring system components and through this approach it is possible to utilize operational data in simulations for continuous prediction of availability during operation. Further, the results of paper B presented in this thesis may extend and improve the model proposed in paper A, if implemented. The results of paper B describe the addition of data stream forecasting to the data stream management fault detection system and have showed improved prediction capabilities. The approach to evaluate the availability in paper A may also be extended and improved through the results presented and demonstrated in paper C. In paper C, a software tool to predict system availability has been developed and demonstrated with data from a real industrial system. The results from the simulation demonstrated, together with the addition of the partitioned multi-objective risk method in paper C, provided an indication of what level of availability may be guaranteed for the product; thus, the approach may be used to compare the availability performance of different designs.

This paper describes the integration of a sensor data stream monitoring system into a proposed functional product model capable of predicting functional availability. Such monitoring systems enable predictive maintenance to be carried out pre-emptive maintenance that is scheduled in response to imminent hardware failure and are in widespread use in industry. The industrial motivation for this research is that agreed upon system availability is a critical element of any business-to-business agreement regarding functional sales. Such a model is important when making strategic choices regarding FPs and can be used to develop a high availability product design through simulation driven development, as well as to provide operational decision support that reflects the current reality to enable optimal availability to be achieved in practice. The proposed model integrates hardware, support system and monitoring system models, and is able to incorporate actual operational data. It has been partly verified based on previous research.

This paper presents a modelling language for representing the details necessary to analyse and model the implementation of maintenance strategies for generic hardware. The maintenance strategy determines which, and when, restorations and inspections should take place whilst the scheduling of maintenance tasks implements these goals. The manner of maintenance strategy implementation therefore has important implications for maintenance cost and other performance metrics. Despite this, maintenance strategy optimisation models found in the literature lack detailed maintenance implementation models, which may lead to inaccurate and misleading results. The presented modelling language permits the representation of all common constraints and outcomes between maintenance tasks that influence task schedules. In doing so, it provides a platform for the future development of maintenance task schedule modelling, planning and decision support tools. The modelling language is demonstrated through application to part of a car braking system

Competition among today’s industrial companies is very high. Therefore, system availability plays an important role and is a critical point for most companies. Detecting failures at an early stage or foreseeing them before they occur is crucial for machinery availability. Data analysis is the most common method for machine health condition monitoring. In this paper we propose a fault-detection system based on data stream prediction, data stream mining, and data stream management system (DSMS). Companies that are able to predict and avoid the occurrence of failures have an advantage over their competitors. The literature has shown that data prediction can also reduce the consumption of communication resources in distributed data stream processing.In this paper different data-stream-based linear regression prediction methods have been tested and compared within a newly developed fault detection system. Based on the fault detection system, three DSM algorithms outputs are compared to each other and to real data. The three applied and evaluated data stream mining algorithms were: Grid-based classifier, polygon-based method, and one-class support vector machines (OCSVM).The results showed that the linear regression method generally achieved good performance in predicting short-term data. (The best achieved performance was with a Mean Absolute Error (MAE) around 0.4, representing prediction accuracy of 87.5%). Not surprisingly, results showed that the classification accuracy was reduced when using the predicted data. However, the fault-detection system was able to attain an acceptable performance of around 89% classification accuracy when using predicted data.