# Systems Engineering: Availability and Reliability
## 2 Reliability and Risk Assessment  
With the rapid development of high integrations in large complex systems, such as aircraft, satellite and railway systems, due to the increasingly complex coupling relationship between components within the system, local disturbances or faults may cause global effects on the system by fault propagation. Therefore, there are new challenges in safety analysis and risk assessment for complex systems. Some of them with their proposed solutions are described below. As presented in [1], to this day, research articles highlighting analytical methods on small data sets are practically limited. From the literature review presented [1], it is revealed that most of the studies conducted thus far using scarce failure data are less. The methods used for reliability analyses for small number data are mostly the Bayesian approach, FMECA and Monte Carlo method. Using these methods, the small failure data of any machine or system can be grouped and easily used for further reliability analysis. Maintenance philosophies involve performing maintenance after given time intervals, typically after a fixed running hour for an engine. In spite of the scheduled maintenance, failure of the engine is inevitable, thereby decreasing the availability of dumpers and reducing the production cost. Reliability analysis of engine subsystems is essential for formulating the maintenance strategies that will reduce the downtime of the engine and enhance its availability. The main obstacle was the deficiency of adequate data for the appropriate statistical analyses. A data set containing a small sample size of failure data limits the possibility of precise decision-making. The study [1] gives specific guidelines for using CBH and meta-analysis testing, which emphasizes the failure data to predict reliability and MTBF. The researchers perform the reliability assessment using the grouped TBF data, during which suitable maintenance strategies could be formed. It provides a roadmap of reliability analysis for any machinery having fewer failure data. For the development of the technical solution--a reliable robotic system to facilitate precision and safety--the ISO 14971 standard for risk management in healthcare devices was followed in [2]. This paper presents the risk management strategy for the development of the technical solution. Moreover, by evaluating the associated risks of the procedure, the medical and technical requirements for the proposed technical solution are defined. It also presents an analytical hierarchy process that is introduced subsequently in a four-phase quality function deployment, which, in turn, leads to the HeRo conceptual design, showing the advantages of the design with respect to risk reduction. The residual risks introduced by the robotic system were also evaluated, showing a reduction in hazard occurrence and severity, which validates the HeRo concept as a possible technical solution. An approach based on the hidden Markov model is proposed [3] for risk performance reasoning. The unobservable state process in the approach aims to model the underlying risk performance, while the observation process was formed from the time series of risk factors. Within the framework, the log-likelihood probability was used as the measure of similarity between historical and current data of risk reasoning factors. Based on scalar quantization regulation and risk performance quantization regulation, the risk performance reasoning approach with different step sizes was conducted on the operational case, the performance of which was evaluated in terms of effectiveness and accuracy. The results obtained show significant improvement in the reasoning capacity and satisfactory performance for numerical risk reasoning and categorical performance reasoning. The proposed model is able to provide a reference for risk performance monitoring and threat pre-warning during the bauxite shipping process.