Systems Reliability Analysis and Complementary Activities

System and machine reliability is an important consideration that must be made when attempting the optimisation of manufacturing capability; it has to be factored into the system design, layout and construction. Consideration has to be given to how reliability factors will influence the required availability of the system and the necessary level of system redundancy to comply with manufacturing and safety considerations. This consideration must be made when commissioning and operating the system, with specific attention paid to the associated maintenance requirements. These considerations and the effect that redundancy engineering can have upon them have been reviewed in the following section indicating the latest ideas on their implementation and improvement.

3.4.1 Availability, Optimisation and System Redundancy

System availability is a consideration which is of paramount importance in the design of an industrial system. As the system becomes more complicated the cost of improving reliability also increases. Redundancy is the main avenue of increasing system availability. Jiang et al. [Jiang et. al. 2005] proposes a genetic algorithm (GA) based optimisation model to improve the design efficiency whilst considering the design constraints. This is carried out through object orientated programming to develop a knowledge based system for the design of a series parallel system. This program becomes an effective tool to decide the related characteristics of each component. The conclusion is reached that the proposed system requires further study to optimise the GA parameters, including data entry and statistical analysis from the design knowledge base. Nourelfath et al. [Nourelfath et al 2007] discusses the redundancy optimisation

problem from a different perspective by assuming that the design goal has achieved its required redundancy through the selection of discrete components available on the market. Nourelfath et al. examines redundancy optimisation of the minimal configuration and maintenance costs of a series parallel multi state systems when under reliability constraints. The maintenance policy specifies the priorities between the system components and the use of a shared maintenance team. The optimisation approach developed by Nourelfath et al. is analytical and uses the universal “z” transform and Markov chain techniques to develop a heuristic model. Future work is recommended in developing a direct optimisation method, which supports the whole maintenance structure

3.4.2 Reliability Analysis in Manufacturing

One of the main objectives for carrying out the literature review was to search for papers that have particular relevance to the Thesis topic of machine reliability in a Hot Strip Mill. One of the few documents in this field is a paper presented by Goode et al.[Goode et al 2000] which considers the operation of a Hot Strip Steel Mill. This is a manufacturing process in which unscheduled stoppages can critically affect plant availability, productivity and product quality. For many years steel companies have practised condition-based monitoring in strategically vital areas such as the Hot Strip Mill. These monitoring methods include vibration analysis, oil and wear debris analysis and performance measurement using numerous techniques to measure parameters such as electric current, temperature etc. The present methods allow maintenance personnel to detect and often diagnose pending equipment failure but they are not able to predict remaining equipment life with any certainty. The authors state that using historical data to predict future performance requires an assumption that historical and current performance is highly verified, in reality this is not the case. A predictive model is proposed which utilises a Weibull distribution to define the expression modelling the failure intervals. This equation is solved using a Monte Carlo approach with the time to failure (TTF) being predicted as a cumulative probability distribution. The paper defines the application of condition monitoring measurements as applied using two separate regimes, designated as the stable and failure zones. In the stable zone condition monitoring methods indicate that the operation is normal and a reliability monitoring

method is used. In the failure zone the condition monitoring methods identify the existence of a problem and both reliability and condition monitoring information are combined to predict the remaining machine life. The paper investigated both simulated and case studies and concluded that the prediction model is highly dependent on both the quality and accuracy of the condition based measurements.

Xie et al. [Xie et al 2009] considers an important parameter in reliability engineering by examining the effects of ageing in a power generating system. The paper identifies that failures can be classified as either repairable random failures or non- repairable ageing “end of life” failures. Xie et al. state that only repairable failures have been considered in most power system’s reliability analysis and that a modelling concept for unavailability due to ageing must be developed. A Normal or Weibull distribution is suggested as the means to estimate the failure probability density function due to the ageing process and a combined model is proposed including calculations for repairable and ageing failures. An example using seven generating units is used to verify the correctness of the constructed model. The results indicate that ageing failures have significant impact on the unavailability of components particularly in the case of older systems.

3.5 Discussion

Reliability analysis in its various forms is a well-established tool used in many industrial applications. It impinges on many aspects of our lives from everyday issues such as domestic transport through to futuristic concepts such as space travel. The problems associated with quantifying reliability are aptly illustrated in a paper by Mendall et al. [Mendall et al 2004]. This paper indicates the significance of reliability in future space exploration by discussing the future requirement of human exploration of Mars, currently envisaged as a 500-day stay at the planets surface. This mission will be incapable of attaining an abort to Earth capability, which means that critical mission systems are specified to perform reliably for over three years. The required reliability level of 99% with a confidence limit of 0.95% would require a test regime for the systems to be operating for 149000 days, in space, without a single failure. This constraint is infeasible and the paper examines the problems of correlating the reliability requirements with current technologies. The conclusion reached is that a rigorous

testing regime including additional Lunar exploration will be required to “prove out” equipment before undertaking the Mars Mission.

Ascher et al. [Ascher et .al. 1984] considers the current state of reliability analysis in respect of the misconceptions and misuse of the approaches he presented in his 1984 book. In a detailed paper Ascher [Ascher 2007] considers that the reliability community is still using widely disparate terminology and notation. These discrepancies primarily surround the conflicting use of failure rates and force of mortality. He strongly advocates added rigour in applied terminology and notation and the use of approaches that recognise the fundamental differences between parts and systems in their models and techniques. The paper stresses the importance of determining whether part or system failure data is being analysed and incorporating the basic differences between parts and systems into data interpretation and subsequent efforts to improve reliability. In reality this appears to be a major concern within the industry e.g. a motor can be system in its own right, but when taking into the context of a manufacturing process which could contain several hundred motors, it would be considered as a part.

Most statistical systems analysis methods referred to in this review are based on one or more of the above processes. The NHPP in its various forms (Power Law etc) accounting for the majority of reliability systems analysis usually with the assumption that the data set forms a stochastic (random) process. The various process derivations have been included for completeness. It can be seen that some of the later analysis methods identified in this review often use some, or several, of the above processes in their analysis.

3.6 Conclusion

The purpose of this review is to identify if there is a reliability engineering analysis method suitable for widespread application to mechanical systems operating in a manufacturing environment.

There is wealth of data available regarding statistical modelling on the reliability of repairable systems: However these are predominantly biased towards statistical investigations into:

 Identifying whether there is a reliability analysis system available for a particular system

 The relative merits of differing reliability analysis methods when applied to a particular system.

 Manufacturing either (a) a derivation of the current reliability analysis techniques or (b) a combination of several techniques in order to create a new reliability analysis technique.

These investigations have predominantly been performed as academic exercises and some have contributed towards the statistical understanding of systems operational behaviour

There is a lack of actual worked examples of complete system analysis. The search of databases for the 2000-2010 periods found less than ten examples. Many of the papers quoted use specific data sets from previous case files, some dating back several decades. However, the majority of the examples for the reliability analysis for repairable systems were based on the Power Law analysis method. This reaffirmed the author’s opinion that the reliability analysis for repairable systems method under review for this application should contain the Power Law method.

As such this review must conclude that the development of a comprehensive approach to reliability engineering analysis suitable for widespread application to mechanical systems operating in a manufacturing environment is needed and that research effort to support this is justified.

The next Chapter identifies the methods used for machine or system failure monitoring currently in use at the Hot Strip Mill in Port Talbot. A spreadsheet application is proposed which can interrogate the failure data base and segregate the data into a format which is suitable for further analysis.