Reliability analysis and optimisation of subsea compression system facing operational covariate stresses

This paper proposes an enhanced Weibull-Corrosion Covariate model for reliability assessment of a system facing operational stresses. The newly developed model is applied to a Subsea Gas Compression System planned for offshore West Africa to predict its reliability index. System technical failure was modelled by developing a Weibull failure model incorporating a physically tested corrosion profile as stress in order to quantify the survival rate of the system under additional operational covariates including marine pH, temperature and pressure. Using Reliability Block Diagrams and enhanced Fusell-Vesely formulations, the whole system was systematically decomposed to sub-systems to analyse the criticality of each component and optimise them. Human reliability was addressed using an enhanced barrier weighting method. A rapid degradation curve is obtained on a subsea system relative to the base case subjected to a time-dependent corrosion stress factor. It reveals that subsea system components failed faster than their Mean time to failure specifications from Offshore Reliability Database as a result of cumulative marine stresses exertion. The case study demonstrated that the reliability of a subsea system can be systematically optimised by modelling the system under higher technical and organisational stresses, prioritising the critical sub-systems and making befitting provisions for redundancy and tolerances

A study of the efficacy of a reliability management system - with suggestions for improved data collection and decision making.

Master's thesis in Risk ManagementProduct reliability is very important especially in the perspective of new product development. Making highly reliable drilling and well equipment is expensive and time-consuming process. But ignoring the product reliability could prove even more costly. Thus the manufacturers need to decide on the best reliability performance that succeeds to create a proper balance between time, cost and reliability factors to ensure the desired results. A reliability management system is a tool that the manufacturers can use to manage this process to produce reliable equipment. However, if this system is not well structured and lacks some important features, it can affect the outcomes of reliability analysis and decision making. A lot of research has been done on creating a good reliability and maintenance database to improve systems reliability in the petroleum industry. Offshore & Onshore Reliability Data (OREDA) and ISO 12224 are part of such research projects. The main objective of this research is to analyze the existing reliability management system (RMS) in Petroleum Technology Company (PTC) in terms of its structure, features, functionality, and the quality of data being recorded in RMS and how it affects decision making. The research was motivated by following issues 1) Reliability Management System of PTC is not automated in terms of extracting data from other sources within company, 2) PTC is missing a specified platform for failure reporting of their equipment, 3) the activities related to data collection and management are not well-organized hence demanding more effort. To analyze these issues, a literature study is performed to review the existing standards in the industry. ISO14224 and OREDA define a very structured database to get easy access to reliability and maintenance data. OREDA database has well-defined taxonomy, boundaries and database structure. Also, it has a well-organized procedure in place to collect and store reliability data. Quality assessment of the data being collected is done through predefined procedures guideline. OREDA have a very consistent list of codes to store language in coding form in the reliability and maintenance database. By reviewing the existing standard in the industry, a few shortcomings have been identified both in the RMS and PTC failure reporting procedures. It is observed that data from the sources is collected by the responsible person but the collection method is usually not tested and planned. Data collection sources, methods and procedures within company or outside the company lack well-defined criteria and data quality assurance processes. Currently, the company is using Field Service Reports (FSR) and company’s other databases as data sources for RMS. A company cannot access client’s system that contains equipment utilization and process-related information. This can lead to missing information or ambiguous data because the data-entry responsible person needs to make assumptions sometimes to complete the missing operational and environmental data. The RMS database structure lacks well-defined taxonomy, design parameters, and adequate failure mode classification. The Failure modes is an important aspect of the high-quality database since it can help in identifying the need for changes to maintenance periodicities, or the need for additional checks. The Offshore & Onshore Reliability Data (OREDA) project participating companies e.g. Statoil can calculate failure rates for selected data populations of within well-defined boundaries of manufacturer, design and operational parameters. These features are missing in RMS database. It is recommended that PTC consider developing a failure reporting database to handle their failure event data in an organized way. For this purpose, failure reporting, analysis, and corrective action system (FRACAS) technique is suggested. FRACAS data from FRACAS database can be used effectively to verify failure modes and failure causes in the failure mode effect and criticality analysis (FMECA). Failure review board in the FRACAS process includes personnel from mix disciplines (design, manufacturing, systems, quality, and reliability engineering) as well as leadership (technical or managerial leads), to make sure that a well-rounded the discussion is performed for particular failure related issues. The Failure Review Board (FRB) analyzes the failures in terms of time, money required corrective actions. And finally, management makes the decisions on basis of identified corrective action. As data quality has a high impact on the outcomes of reliability analysis through reliability management system. To have a good data quality, data collecting procedures and process management should be well-organized. It is crucial to performed data quality assessment on collected data. A data mining technique is discussed as a part of suggestion to improve data quality in RMS database. Once data is stored in RMS database a data mining method; data quality mining can help to assess the quality of data in a database. This is done by applying a data mining (DM) tool to look at interesting patterns of data with the purpose of quality assessment. Various data mining model is available in the market but PTC needs to select DM model which suits best their business objectives. RMS database is hard-wired so it is difficult to change its features and database structure. However, if PTC emphasize on improving failure reporting procedures and data quality in data sources locating within the company, it will directly and positively affect the data quality in RMS and the results of data analysis in RMS. This, in turn, can improve their decision-making the process regarding new product development and redesigning the existing products

Predictive maintenance of rotational machinery using deep learning

This paper describes an implementation of a deep learning-based predictive maintenance (PdM) system for industrial rotational machinery, built upon the foundation of a long short-term memory (LSTM) autoencoder and regression analysis. The autoencoder identifies anomalous patterns, while the latter, based on the autoencoder’s output, estimates the machine’s remaining useful life (RUL). Unlike prior PdM systems dependent on labelled historical data, the developed system doesn’t require it as it’s based on an unsupervised deep learning model, enhancing its adaptability. The paper also explores a robust condition monitoring system that collects machine operational data, including vibration and current parameters, and transmits them to a database via a Bluetooth low energy (BLE) network. Additionally, the study demonstrates the integration of this PdM system within a web-based framework, promoting its adoption across various industrial settings. Tests confirm the system's ability to accurately identify faults, highlighting its potential to reduce unexpected downtime and enhance machinery reliability

Computer-Aided System for Wind Turbine Data Analysis

Context: The current work on wind turbine failure detection focuses on researching suitable signal processing algorithms and developing efficient diagnosis algorithms. The laboratory research would involve large and complex data, and it can be a daunting task. Aims: To develop a Computer-Aided system for assisting experts to conduct an efficient laboratory research on wind turbine data analysis. System is expected to provide data visualization, data manipulation, massive data processing and wind turbine failure detection. Method: 50G off-line SCADA data and 4 confident diagnosis algorithms were used in this project. Apart from the instructions from supervisor, this project also gained help from two experts from Engineering Department. Java and Microsoft SQL database were used to develop the system. Results: Data visualization provided 6 different charting solutions and together with robust user interactions. 4 failure diagnosis solutions and data manipulations were provided in the system. In addition, dedicated database server and Matlab API with Java RMI were used to resolve the massive data processing problem. Conclusions: Almost all of the deliverables were completed. Friendly GUI and useful functionalities make user feel more comfortable. The final product does enable experts to conduct an efficient laboratory research. The end of this project also gave some potential extensions of the system

Modelling and managing reliability growth during the engineering design process

[This is a keynote speech presented at the 2nd International Conference on Design Engineering and Science, discussing modelling and managing reliability growth during the engineering process.] Reliability is vital for safe and efficient operation of systems. Decisions about the configuration and selection of parts within a system, and the development activities to prove the chosen design, will influence the inherent reliability. Modelling provides a mechanism for explicating the relationship between the engineering activities and the statistical measures of reliability so that useful estimates of reliability can be obtained. Reliability modelling should be aligned to support the decisions taken during design and development. We examine why and how a reliability growth model can be structured, the type of data required and available to populate them, the selection of relevant summary measures, the process for updating estimates and feeding back into design to support planning decisions. The modelling process described is informed by our theoretical background in management science and our practical experience of working with UK industry

Sensitivity Analysis for a Scenario-Based Reliability Prediction Model

As a popular means for capturing behavioural requirements, scenariosshow how components interact to provide system-level functionality.If component reliability information is available, scenarioscan be used to perform early system reliability assessment. Inprevious work we presented an automated approach for predictingsoftware system reliability that extends a scenario specificationto model (1) the probability of component failure, and (2) scenariotransition probabilities. Probabilistic behaviour models ofthe system are then synthesized from the extended scenario specification.From the system behaviour model, reliability predictioncan be computed. This paper complements our previous work andpresents a sensitivity analysis that supports reasoning about howcomponent reliability and usage profiles impact on the overall systemreliability. For this purpose, we present how the system reliabilityvaries as a function of the components reliabilities and thescenario transition probabilities. Taking into account the concurrentnature of component-based software systems, we also analysethe effect of implied scenarios prevention into the sensitivity analysisof our reliability prediction technique

Uncertainty explicit assessment of off-the-shelf software: Selection of an optimal diverse pair

Assessment of software COTS components is an essential part of component-based software development. Sub-optimal selection of components may lead to solutions with low quality. The assessment is based on incomplete knowledge about the COTS components themselves and other aspects, which may affect the choice such as the vendor's credentials, etc. We argue in favor of assessment methods in which uncertainty is explicitly represented (`uncertainty explicit' methods) using probability distributions. We have adapted a model (developed elsewhere by Littlewood, B. et al. (2000)) for assessment of a pair of COTS components to take account of the fault (bug) logs that might be available for the COTS components being assessed. We also provide empirical data from a study we have conducted with off-the-shelf database servers, which illustrate the use of the method