The main function of a heavy truck is to transport goods. Furthermore,the truck is directly operated by a driver, who has several additional functionalrequirements, of both ergonomic and communicative characters. Transport is a trustbusiness and today’s just-in-time delivery systems rely on getting the goods on time.Product reliability, which is the ability of a system to perform according to itsfunctional targets, is consequently a crucially important property for a heavy truck.This paper proposes a structure for a system reliability model that integrates differentand complementary representations, such as Function-Means trees and DesignStructure Matrix. The novelty of the presented approach is that it treats andintegrates the technical and the human subsystems through the human-technicalsystem interfaces in an extended DSM. The proposed reliability systems approachis verified with a component analysis case study of a truck cab and driver system.

The main function of a heavy truck is to transport goods, with ton-kilometers/year as an example of a major quantitative performance measure. Furthermore, the truck is directly operated by a driver, who has several additional functional requirements, of both ergonomic and communicative characters. Failure of these functions may be a subjective experience, differing between drivers, but the failures are still important. Today's just-in-time delivery systems rely on getting the goods on time, and this requires high availability. Availability is reduced not only by technical failures but also by subjectively experienced failures, because these also require repairs, or downtime. Product reliability is a systems property that cannot be attributed to a single component. It is in many cases related to interaction between components, or to interaction between humans and the technical system, in the case of subjectively experienced failures. Reliability assessments of systems with interactive functions require a system model that includes the interfaces between the technical system and human features that are carriers of interactive functions. This paper proposes a model of system architecture, for the purpose of reliability assessments, that integrates different and complementary representations, such as function-means diagrams and a design structure matrix. The novelty of the presented approach is that it treats and integrates the technical and the human subsystems through the human-technical system interfaces. The proposed systems reliability approach is described and verified with a component analysis case study of an extended truck cab and driver system.

Product reliability is a systems property that cannot be attributed to a single component. It is in many cases related to interactions between components, or to interactions between humans and the technical system. Product functionality includes both technical functions, like structural integrity and interactive functions, like ergonomics. Reliability assessments in the early stages of the development process are valuable, since design changes cost significantly less if made early. System reliability tests can only be made towards the end of the development process, but early estimates can be based on test data from component tests and function tests. Operating conditions often vary between component tests and system tests. Therefore, reliability assessments where data from one situation is used to predict reliability in a different situation must take this variation into account. We investigate how this can be done for both technical and interactive functions. The study is made in the context of system reliability for heavy trucks, where both technical functions and interactive functions affect product reliability. Two cases have been assembled from test data, one concerning a component on a truck cab, the other an interactive function of a truck. Two reliability estimation methods have been evaluated to investigate if the methods can be used for interactive functions as well as for technical functions. We conclude that a method for reliability estimation of interactive functions must be able to model increased uncertainty due to intrapersonal variation.

Heavy truck customers attach great importance to reliability, which make reliability assessments essential in product development projects. Since changes are easier and less expensive in early project stages, early reliability assessments are valuable. At these early stages, complete vehicle testing cannot yet be made. System reliability assessments must be made based on test data from component and subsystem tests, sometimes performed with different operating conditions than the system will be used in. Test data must be translated to the new situation, which requires information about how various factors affect reliability. Furthermore, the uncertainty in the forecast increases when the assessment is made for new operating conditions. Therefore, we also need information about how uncertainty propagates. The question is how this translation can be made, when data is sparse and expert judgement must be used, and how the increasing uncertainty can be reasonably modelled. In this paper, current methods to take into account varying operating conditions have been reviewed, and four methods have been tested in a case study. These methods are one based on fuzzy logic, a first-order second-moment reliability method (VMEA), and two variants of the proportional hazards model. The study shows that several methods are capable of handling sparse data, but only VMEA can model how uncertainty increases when operating conditions vary. It has however the drawback of being quite sensitive to uncertainty in the input data.