SPACECRAFT RELIABILITY CALCULATIONS USING STATISTICAL REGULARITIES IN OCCURRENCES OF FAILURES IN DEVICES, UNITS AND ASSEMBLIES DURING THEIR OPERATION

The paper addresses reliability analysis of manned spacecraft with the use of statistical regularities in in-flight failures of their devices, units and assemblies. It formulates validity criteria for using a device failure in reliability analysis, proposes a method for analyzing and classifying failures which enables factoring in different types of failures in reliability analyses. It considers a hypothesis of the absence of statistically significant differences in probabilities of individual valid failures and demonstrates the feasibility of its adoption with the use of dispersion analysis. A method is developed for evaluating product reliability using a functional relationship between reliability and the number of failures occurring in flight which makes it possible to significantly simplify reliability analysis for complex products, to establish the number of in-flight failures that is acceptable from the standpoint of the product reliability requirements, to study various product architectures from the standpoint of reliability criteria. It proposes a method for evaluating the lower boundary for the probability of manned spacecraft completing their missions based on the failure modes, effects and criticality analysis, and demonstrates the feasibility of optimizing the product redundancy scheme based on the fault tolerance requirements. Key words: manned spacecraft, flight, failure, fault tolerance, classification of failures, reliability, probability of failure-free operation, statistical analysis, dispersion analysis.

Optimal allocation of product reliability using novel multi-population particle swarm optimization algorithm

Reliability optimal allocation is a critical method in engineering and operation fields to improve and ensure product quality. However, conventional model generally uses the single objective with research and development (R & D) cost or product reliability. Moreover, the popular particle swarm optimization (PSO) algorithm solving the model has insufficient global search ability. In this paper, a novel reliability optimal allocation method is developed based on double-objective model and multi-population PSO (MPPSO) algorithm. The cost function is established considering all the cost related to reliability using analytical hierarchical process (AHP) and ordered weighted averaging (OWA) operator. Multi-population particle swarm optimization algorithm is developed based on information exchange mechanism among subpopulations. In the algorithm, the inertia weight is optimized by chaotic map, and different mutation operations are carried out on the subpopulations. An illustrative example, which implements to computer numerical control (CNC) grinding machine, is presented to illustrate the practicality and application of the proposed method. Finally, the comparison and analysis are implemented to show the advantage of proposed model and MPPSO algorithm.

Root cause identification approach using decomposition of QFD and extended RPN for product manufacturing reliability degradation

Reliability is reflected in product during manufacturing. However, due to uncontrollable factors during production, product reliability may degrade substantially after manufacturing. Thus, root cause analysis is important in identifying vulnerable parameters to prevent the product reliability degradation in manufacturing. Therefore, a novel root cause identification approach based on quality function deployment (QFD) and extended risk priority number (RPN) is proposed to prevent the degradation of product manufacturing reliability. First, the connotation of product manufacturing reliability and its degradation mechanism are expounded. Second, the associated tree of the root cause of product manufacturing reliability degradation is established using the waterfall decomposition of QFD. Third, the classic RPN is extended to focus on importance to reliability characteristics, probability, and un-detectability. Furthermore, fuzzy linguistic is adopted and the integrated RPN is calculated to determine the risk of root causes. Therefore, a risk-oriented root cause identification technique of product manufacturing reliability degradation is proposed using RPN. Finally, a root cause identification of an engine component is presented to verify the effectiveness of this method. Results show that the proposed approach can identify the root cause objectively and provide reference for reliability control during production.

Early Detection of Product Reliability Based on the Parameters of the Production Line and Warranty Data

Reliability is one of the key dimensions of the quality of services and products that should be always evaluated. Growth and development of industries can be achieved by appropriate reliability engineering of products. Companies should evaluate and predict the reliability of products and accordingly find and fix the potential problems. In this regard, early detection of reliability problems based on the parameters of the production line or quality test results can prevent future warranty costs. Early detection of reliability problems based on production process and test data has not gained much attention in the literature. Therefore, an early detection model for predicting the reliability of products according to their quality test results is proposed in this research. For this purpose, hot test and warranty data of car engines manufactured by an automotive company are utilized. This data are prepared to predict engine reliability after preprocessing and removing inefficient data. Then, engines are divided into two homogeneous clusters using particle swarm optimization (PSO) clustering algorithm. Afterwards, the data in these clusters are used to feed the Artificial Neural Network (ANN) to predict the reliability of the engines. The obtained results show that the proposed ANN-based method is able to predict the reliability of the engines based on engine kilometers operated and hot test results. Also, it is shown that the proposed method outperforms the Cox proportional hazards model which has previously been used for early detection of product reliability.