The paper addresses the need for innovation in order to achieve sustainable growth and business development within the manufacturing industry, and further how that can be enabled by striving towards functions. Adopting an open perspective, the paper proposes a function innovation model involving academia, potential function providers and customers in order to create a long-term win-win situation between function providers and their customers.

Based on empirical studies combined with a literature review, the paper proposes a comprehensive framework defining Functional Products (FP) through their constituents. The framework adds additional specificity to the literature by identifying and discussing existing and emerging constituents of FP, shedding further light on what is needed to create a long and trustful win-win situation between providers and customers in an FP context

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.

The paper addresses the potential use of cloud services in Functional Products (FP) and its possible implications for availability. Further, how the implications for availability can be understood via modelling and simulation is addressed. The paper adds further specificity to literature by indicating the FP constituents for which cloud services are applicable and adequate.

A functional product (FP) comprises of an integrated package of hardware and support services sold under a performance-based contract. A barrier to the adoption of FP is the lack of tools for obtaining predictions of availability and support costs during product development. A previous paper by the authors described a simulation-driven development strategy for designing FP that are optimised for functional availability and support costs. This iterative strategy involves representing the FP design in a modelling language; using a software code to automatically generate and analyse a simulation model from this representation to produce detailed performance predictions; and using these predictions as feedback to improve the design. The use of a modelling language facilitates the representation of the design details within the hardware and support system that influence availability and support costs. This includes the maintenance process design, maintenance strategy design and maintenance resource availability design. In this paper, an overview of a modelling language the authors have developed for this purpose is described.

During the last few decades, and in order to increase product development efficiency, simulations strategies have been developed to guide designers towards better solutions rather than to verify suggested and basically unevaluated solutions. Such approaches are often called Simulation Driven Design (SDD), which is of interest in this review. The objective of this paper is to show the research evolution of SDD and identify the state of the art in SDD methodology. The literature review comprises several hundred references, of which 79 are included in this paper. The results comprise the state of the art in simulation driven design including the history, various definitions, criteria and effects of using SDD approaches.

The development process for a Functional Product (FP) is complex and there is a need to coordinate, monitor, control and share information as well as to communicate properly among the parties involved in the process. This paper proposes a conceptual development process to manage the FP development, including development of hardware, software, service support system, and how to manage the operation of an FP. Further, challenges related to the integrated development of FPs are also discussed.

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.