Maintenance is a continuous process implemented by Facilities Management (FM) providers as one of their core functions to effectively manage and maintain critical assets throughout the whole life of a building and prevent downtime to essential systems. The data, technology and systems of Condition Monitoring (CM) are relevant to FM building maintenance, yet the use of CM solutions for predictive maintenance regimes are rarely implemented in practice within the industry.
At Skanska, we build for a better society, and we actively collaborate with forward thinking organisations such as Cybula in order to implement some of the highest levels of digital innovation i.e. the implementation of intelligent CM solutions for predictive maintenance. This presentation will outline some case studies of these collaborative endeavours. More specifically, it will focus on projects that successfully demonstrates the implementation and application of innovative CM technologies within FM maintenance, and provides substantial CAPEX and OPEX savings. Furthermore, it will discuss a collaborative research project being undertaken which proposes a novel low-cost Internet of Things (IoT) based solution that combines Artificial Intelligence (machine learning and associative memory models) and LoRa communication technology for remote monitoring capability. This solution also aims to combine traditional FFT spectra analysis with memory base models that can learn system behaviour and create dynamic abnormality detection.
Bio - Tom Jackson
Dr. Tom Jackson is CEO of CYBULA, a York based SME specialising in data mining and artificial intelligence methods for industrial applications of machine reasoning. He has a degree in Electronics (Salford University) and a PhD in Computer Science (University of York), and his research interests lie in the field of neural networks and knowledge representation. He is the author of a standard text on neural computing and has extensive publications in the area. Cybula systems are based on the novel use of binary associative memory models, which provide a flexible and powerful way to develop data driven models of assets. He has managed several high profile projects on the development and application of these methods in commercial applications, spanning aerospace, transport, medical, power generation and facilities management sectors.
Bio - Ruhul Amin
Dr. Ruhul Amin is Digital Programme Lead within Skanska UK’s central Digitalisation team. He has extensive experience developing, managing and deploying real world Big Data and Internet of Things (IoT) projects within public and private sectors. He is a Member of the British Institute of Non-Destructive Testing (BINDT), and winner of BINDT’s 2017 Condition Monitoring Innovation Award. Ruhul has a Masters of Research and Engineering Doctorate from University College London. His doctorate level research specialism is Condition-Based Maintenance and Monitoring of critical mechanical equipment in the built environment.