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Engineering increasingly relies on intelligent monitoring systems to improve performance, maximise reliability and prevent failure. From monitoring rotating machinery in industry to wearable sensors that track human movement and health, sensing technologies are transforming how engineers understand and manage complex systems. This module explores the principles and applications of condition monitoring across both engineering and biomechanical systems.

You will investigate a range of maintenance strategies, international standards for condition monitoring and system integration approaches while developing an understanding of advanced sensors, instrumentation and data acquisition techniques. The module also introduces advanced signal processing methods, enabling you to extract meaningful information from measurement data and assess the health and performance of engineering systems. Throughout the module, you will strengthen your analytical, diagnostic and systems engineering skills by applying sensing technologies to real engineering applications.

By the end of the module, you will be able to evaluate and design condition monitoring systems using advanced sensing and data analysis techniques, preparing you for careers in structural health monitoring, condition-based maintenance, robotics, healthcare technologies and smart engineering systems.

Engineering increasingly relies on intelligent monitoring systems to improve performance, maximise reliability and prevent failure. From monitoring rotating machinery in industry to wearable sensors that track human movement and health, sensing technologies are transforming how engineers understand and manage complex systems. This module explores the principles and applications of condition monitoring across both engineering and biomechanical systems.

You will investigate a range of maintenance strategies, international standards for condition monitoring and system integration approaches while developing an understanding of advanced sensors, instrumentation and data acquisition techniques. The module also introduces advanced signal processing methods, enabling you to extract meaningful information from measurement data and assess the health and performance of engineering systems. Throughout the module, you will strengthen your analytical, diagnostic and systems engineering skills by applying sensing technologies to real engineering applications.

By the end of the module, you will be able to evaluate and design condition monitoring systems using advanced sensing and data analysis techniques, preparing you for careers in structural health monitoring, condition-based maintenance, robotics, healthcare technologies and smart engineering systems.

This module explores from traditional conditioning monitoring of machinery to biomechanical systems (i.e. sensors to monitor body forces and motions). It covers condition monitoring strategies, including international standards, monitoring procedures and system integration. In addition, advanced sensors and sensing methods, as well as advanced signal processing techniques are covered.

This module explores from traditional conditioning monitoring of machinery to biomechanical systems (i.e. sensors to monitor body forces and motions). It covers condition monitoring strategies, including international standards, monitoring procedures and system integration. In addition, advanced sensors and sensing methods, as well as advanced signal processing techniques are covered.

This module explores from traditional conditioning monitoring of machinery to biomechanical systems (i.e. sensors to monitor body forces and motions). It covers condition monitoring strategies, including international standards, monitoring procedures and system integration. In addition, advanced sensors and sensing methods, as well as advanced signal processing techniques are covered.

The Advanced Skills Portfolio documents your development in and mastery of a range of subject-specific, transferable and generic skills during the first two years of the research phase of the IPhD Applied Linguistics/English Language Teaching programme. It is compiled as part of your preparation for your PhD Confirmation, building on a range of different learning activities and experiences undertaken during this doctoral programme. You will be invited to start planning your Advanced Skills Portfolio in your first year of the programme.

Through your ongoing preparation of the Advanced Skills Portfolio over parts two and three of the IPhD, you will reflect critically on your developing research expertise, professional training, personal development, and employability. This will help identify areas of your development to focus on in the latter stages of the doctoral journey, supporting you in the transition to a future career as an academic, a researcher, a teacher, or in other employment sectors. The written portfolio is formally assessed.

The Advanced Skills Portfolio documents your development in and mastery of a range of subject-specific, transferable and generic skills during the first two years of the research phase of the IPhD Applied Linguistics/English Language Teaching programme. It is compiled as part of your preparation for your PhD Confirmation, building on a range of different learning activities and experiences undertaken during this doctoral programme. You will be invited to start planning your Advanced Skills Portfolio in your first year of the programme.

Through your ongoing preparation of the Advanced Skills Portfolio over parts two and three of the IPhD, you will reflect critically on your developing research expertise, professional training, personal development, and employability. This will help identify areas of your development to focus on in the latter stages of the doctoral journey, supporting you in the transition to a future career as an academic, a researcher, a teacher, or in other employment sectors. The written portfolio is formally assessed.

The human sciences are evolving fast to incorporate new forms of data and powerful new analysis tools.
Advances in machine learning have allowed huge improvements in our ability to predict individual characteristics and preferences, while our interactions with networked devices and online stores and services produce ‘digital breadcrumbs’ that can lead us to new insights about behaviour. The types of sources which social researchers now investigate include data on trends in search terms, online review databases, and many others.
At the same time, these methods and data sources generate important ethical issues that we must consider.
This module will provide students with crucial skills in data manipulation and visualisation, programming and the application of machine learning methods to social data. These skills have wide-ranging applications in research business, and the public sector.

The human sciences are evolving fast to incorporate new forms of data and powerful new analysis tools.
Advances in machine learning have allowed huge improvements in our ability to predict individual characteristics and preferences, while our interactions with networked devices and online stores and services produce ‘digital breadcrumbs’ that can lead us to new insights about behaviour. The types of sources which social researchers now investigate include data on trends in search terms, online review databases, and many others.
At the same time, these methods and data sources generate important ethical issues that we must consider.
This module will provide students with crucial skills in data manipulation and visualisation, programming and the application of machine learning methods to social data. These skills have wide-ranging applications in research business, and the public sector.

The human sciences are evolving fast to incorporate new forms of data and powerful new analysis tools.
Advances in machine learning have allowed huge improvements in our ability to predict individual characteristics and preferences, while our interactions with networked devices and online stores and services produce ‘digital breadcrumbs’ that can lead us to new insights about behaviour. The types of sources which social researchers now investigate include data on trends in search terms, online review databases, and many others.
At the same time, these methods and data sources generate important ethical issues that we must consider.
This module will provide students with crucial skills in data manipulation and visualisation, programming and the application of machine learning methods to social data. These skills have wide-ranging applications in research business, and the public sector.