Ray Venkataraman and Richard Unkle
The purpose of this article is to determine if there is a linkage between the army material systems analysis activity (AMSAA) reliability growth models at the system and subsystem…
Abstract
Purpose
The purpose of this article is to determine if there is a linkage between the army material systems analysis activity (AMSAA) reliability growth models at the system and subsystem levels and at the subsystem and functional levels of indenture. If such a linkage exists, how this information can be used when tracking reliability of fielded systems to provide early warning signals to detect unwanted reliability problems at lower levels, where improvements are typically made.
Design/methodology/approach
Actual performance data from large equipments were analyzed for several groupings of such equipment, where equipment age and generation of design determined the groupings. For each dataset, the system‐level trend was measured using the AMSAA model for three different cases. For each of the three cases, all subsystem trends were measured, in addition to the system‐level trend. This was done to see if any relationship exists, as hypothesized, between system and subsystem trends. Data were analyzed using three different combinations of time periods to facilitate a later investigation of the predictive capabilities of the AMSAA model.
Findings
Results indicate that, for large complex systems or equipment, there appears to be a linkage at the subsystem and functional levels. However, no such hazard rate trending relationship exists between the system and subsystem levels. Based on these results, it can be cautiously concluded that, in terms of reliability, in subsystems that exhibit an increasing trend in unwanted issues, there is a higher likelihood of a function that is driving such a trend.
Practical implications
A useful approach in reliability studies to see if the hazard rate trending relationships between levels of hardware indenture exists as one progresses toward the system‐level. For large complex systems or equipments, such as the one represented here, the initial results indicate that the answer is no, at least between the system and subsystem levels of indenture. However, there is strong evidence that such relationships are valid between subsystems and their functions.
Originality/value
This paper advances existing knowledge in the area of reliability analysis by exploring the use of army material systems analysis activity (AMSAA) reliability growth models to determine linkages between the system and subsystem levels and at the subsystem and functional levels of indenture. If such a linkage exists, it is possible to determine how this information can be used to provide early warning signals when tracking reliability of fielded systems.
Details
Keywords
Richard Unkle and Ray Venkataraman
Historically, reliability of systems has been tracked based on a common assumption that, at the system level, the failure rate follows the exponential distribution, and is…
Abstract
Historically, reliability of systems has been tracked based on a common assumption that, at the system level, the failure rate follows the exponential distribution, and is therefore assumed to be constant over the useful life of the system. However, this method, while adequate for many purposes, does not necessarily provide the early warning system that many companies need to stay ahead of expensive quality or reliability fixes. This paper presents a new method that provides the needed early warning, at a reasonable analysis cost, by combining the use of two reliability distributions for the purpose of analyzing fielded systems. In particular, this paper describes a hypothesized relationship between a key parameter contained in the Weibull distribution and within the Army Material Systems Analysis Activity (AMSAA) reliability growth model. Actual data from General Electric Transportation Systems (GETS) were used to explore this relationship. The results suggest that there indeed exists a significant relationship between the two models and both can be used in tandem to track reliability of systems.