Reliability Prediction Based on Variation Mode and Effect Analysis

Abstract

The possibility of predicting the reliability of hardware for both components and systems is important in engineering design. Today, there are several methods for predicting the reliability of hardware systems and for identifying the causes of failure and failure modes, for example, fault tree analysis and failure mode and effect analysis.Many failures are caused by variations resulting in a substantial effect on safety or functional requirements. To identify, to assess and to manage unwanted sources of variation, a method called probabilistic variation mode and effect analysis (VMEA) has been developed. With a prescribed reliability, VMEA can be used to derive safety factors in different applications. However, there are few reports on how to derive the reliability based on probabilistic VMEA, especially for transmission clutch shafts.Hence, the objective of this article was to show how to derive system reliability based on probabilistic VMEA. In particular, wheel loader automatic transmission clutch shaft reliability is investigated to show how different sources of variation affect reliability.In this article, a new method for predicting system reliability based on probabilistic VMEA is proposed. The method is further verified by a case study on a clutch shaft. It is shown that the reliability of the clutch shaft was close to 1.0 and that the most significant variation contribution was due to mean radius of the friction surface and friction of the disc.

PAL-VMEA: A Novel Method for Enhancing Decision-Making Consistency in Maintenance Management

“…The method systematically assesses how Noise Factors (NF) affect the Key Product Characteristics (KPC) of a system's components. The VMEA result is an index, the Variation Risk Priority Number (VRPN), which indicates the transfer of the variation of these NFs to the analyzed system, supporting the proposal of solutions that could minimize such effects and increase the robustness of the system [26][27][28]. Considering the DMP context, these characteristics make the method a strong candidate for application in the maintenance management decision-making process.…”

“…Pavasson et al [28] stated that the possibility of predicting the reliability of hardware for both components and systems is important in engineering design. So VMEA was used to investigate how different sources of variation affected the reliability of a wheel loader automatic transmission clutch shaft.…”

Big Data-Oriented Product Infant Failure Intelligent Root Cause Identification Using Associated Tree and Fuzzy DEA

“…Masayuki et al [12] used the Axiomatic design to conduct failure data analysis and stressed that complex and coupled designs are the root causes of engineering failures. Pavasson et al [13] proposed a variation mode and effect analysis (VMEA) method to identify the source of variation by identifying the key characteristics in the product development process that can be used as specific targets for quality improvement. Shukla et al [14] proposed a methodology for optimal sensor allocation for root cause analysis that maximizes key characteristics, which include product characteristics and control characteristics in a multi-station assembly process.…”

Challenges and Opportunities within Simulation-driven Functional Product Development and Operation

“…The possibility of predicting the reliability of hardware for both components and systems is important in robust design engineering. Many failures are caused by variations, resulting in a substantial effect on safety or functional requirements; therefore a new method has been derived for predicting system reliability based on probabilistic Variation Mode and Effect Analysis (VMEA) i.e., how different sources of variation affect reliability [8]. Availability is a function of reliability and maintainability [9].…”