Throughout this module you will learn how condition monitoring of engineering assets is performed, with a focus on applications such as in energy systems. Through case studies, underpinned with theory, you will examine and implement aspects of condition monitoring, fault diagnosis, performance assessment, systems modelling and forecasting – all of which are integral aspects in the continuous improvement of modern engineering systems. The theoretical underpinning will include an introduction to signal processing, data acquisition and differing systems modelling approaches. The module will familiarise you with the techniques aimed towards the operational improvement of modern engineering systems. 

LO1. Recognise the critical components within an engineering system and develop suggestions for the design of condition monitoring systems which will reflect the system structure and requirements.  

LO2. Categorise and examine measurements and signals captured by a given condition monitoring system and use this information to hypothesise about level of degradation within a system.   

LO3. Compute a range of signal processing operations to extract information from a set of measurements and to reduce the data storage requirements for trending and monitoring a system’s performance.  

LO4. Assess the data storage requirements for a given condition monitoring system and critique the implementation of condition monitoring systems presented as part of a series of case studies.    

LO5. Categorise different systems modelling and forecasting approaches and critically assess the appropriateness of each for various applications with differing requirements.  

LO6. Relate the outputs of systems modelling and forecasting technique to system condition and predict time to failure for a given component.  

LO7. Generate suggestion for the implementation of condition monitoring systems including appropriate instrumentation, systems measurements, data acquisition systems, signal processing approaches and data utilisation within models and forecasting methods.  

The module will be delivered through a blend of face-to-face teaching (such as lectures, guided study, tutorials, and formative feedback sessions), and online learning material (such as recorded lectures, quizzes, numerical examples and sample tutorial problems). A number of industrial Case Studies are structured within the module to illustrate some of the key themes; these case studies reflecting both documented evidence and experience of the lecturers associated with the module. In this way, you can gain a better understanding of some of the many issues associated with this broad-based subject. 

Structured problems are also integrated within the module notes and you are encouraged to discuss their solutions within the class tutorial environment. You are expected to undertake all the tutorial questions and to relate them to the examples used during classes. This strengthening of knowledge in each module theme will help you towards meeting all learning outcomes. 

Module content is presented via a series of interactive notes which utilise MATLAB live scripts to support learning with code implementation suggestions where figures and animations are produced interactively. There is an option to receive notes through a static PDF format if generative code based scripts are preferred.  

• Through tutorial sessions, you will practice the ability to contribute positively within group discussions of the material communicating complex ideas with confidence.  

• Through the series of case studies and tutorial example questions, you will identify and analyse complex issues arising in the continued optimisation of modern engineering applications such as energy systems.   

• You will learn to apply effective solutions to given problems learning from feedback and peer-based reflections.  

• You will learn to effectively apply signal processing, modelling, and forecasting approaches to imaginatively generate condition monitoring solutions.  

• More broadly you will learn to understand the organisational framework under-pinning the continued improvement and monitoring of engineering systems, identifying how and why condition monitoring approaches are utilised from an organisational standpoint.  

The module is assessed via a single 2-hour examination at the end of the Semester (LO 1-7). The exam paper is composed of two questions which are compulsory. 

THE OPPORTUNITY FOR REASSESSMENT IN THIS MODULE: 

The re-assessment for this module will consist in a 2-hour written examination. 

Opportunities for re-assessment is only permitted provided you have not failed more credit than in the resit rule adopted by your programme.  If the amount of credit you have failed is more than permitted by the relevant resit rule, you may be permitted to repeat study if you are within the threshold set for the Repeat rule adopted by your programme.  You will be notified of your eligibility to resit/repeat any modules after the Examining Board in the Summer period.  

All resit assessments will be held in the Resit Examination period, prior to the start of the following academic session. 

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• Monitoring and fault diagnosis of plant: the need, maintenance strategies. 

• Condition monitoring (CM) methods: Introduction, electrical, fluid, mechanical sensors, wear debris analysis, vibration of mechanical components. 

• Development stages in creating a CM system. 

• Signal processing techniques and examples, contributions from modelling, correct selection of parameters. 

• Vibration monitoring, frequency spectrums and time series analysis. 

• Machine tool and renewable energy monitoring examples. 

• CM and prognostics and health management (PHM): recent trends, fault and energy efficiency monitoring. 

• CM computing platforms and tools. 

• Structural health monitoring and acoustic emission techniques in aerospace applications. 

• Using MATLAB and Simulink to develop CM algorithms and modelling of engineering systems - Optional (Not Examined).