A Methodology for Prognostics under the Conditions of Limited Failure Data Availability

When failure data are limited, data-driven prognostics solutions underperform since the number of failure data samples is insufficient for training prognostics models effectively. In order to address this problem, we present a novel methodology for generating failure data which allows training datasets to be augmented so that the number of failure data samples is increased. In contrast to existing data generation techniques which duplicate or randomly generate data, the proposed methodology is capable of generating new and realistic failure data samples. The methodology utilises the conditional generative adversarial network and auxiliary information pertaining to failure modes to control and direct the failure data generation process. The theoretical foundation of the methodology in a non-parametric setting is presented and we show that it holds in practice using empirical results. The methodology is evaluated in a real-world case study involving the prediction of air purge valve failures in heavy-trucks. Two prognostics models are developed using the gradient boosting machine and random forest classifiers. When these models are trained on the augmented training dataset, they outperformed the best solution previously proposed in the literature for the case study by a large margin. More specifically, costs due to breakdowns and false alarms are reduced by 44%The first author’s PhD research is funded by the EPSRC through a Doctoral Training Partnership grant (EP/M508007/1). This research was partly supported by the Next Generation Digital Infrastructure project (EP/R004935/1) funded by the EPSRC and B

Generating real-valued failure data for prognostics under the conditions of limited Data Availability

Data-driven prognostics solutions underperform under the conditions of limited failure data availability since the number of failure data samples is insufficient for training prognostics models effectively. In order to address this problem, we present a novel methodology for generating real-valued failure data which allows training datasets to be augmented so that the number of failure data samples is increased. In contrast to existing data generation techniques which duplicate or randomly generate data, the proposed methodology is capable of generating new and realistic failure data samples. To this end, we utilised the conditional generative adversarial network and auxiliary information pertaining to the failure modes. The proposed methodology is evaluated in a real-world case study involving the prediction of air purge valve failures in heavy trucks. Two prognostics models are developed using gradient boosting machine and random forest classifiers. It is shown that when these models are trained on the augmented training dataset, they outperform the best prognostics solution previously proposed in the literature for the case study by a large margin. More specifically, costs due to breakdowns and false alarms are reduced by 44%.EPSR

Developing a Digital Twin at Building and City Levels: A Case Study of West Cambridge Campus

A digital twin (DT) refers to a digital replica of physical assets, processes, and systems. DTs integrate artificial intelligence, machine learning, and data analytics to create living digital simulation models that are able to learn and update from multiple sources as well as represent and predict the current and future conditions of physical counterparts. However, current activities related to DTs are still at an early stage with respect to buildings and other infrastructure assets from an architectural and engineering/construction point of view. Less attention has been paid to the operation and maintenance (O&M) phase, which is the longest time span in the asset life cycle. A systematic and clear architecture verified with practical use cases for constructing a DT would be the foremost step for effective operation and maintenance of buildings and cities. According to current research about multitier architectures, this paper presents a system architecture for DTs that is specifically designed at both the building and city levels. Based on this architecture, a DT demonstrator of the West Cambridge site of the University of Cambridge in the UK was developed that integrates heterogeneous data sources, supports effective data querying and analysis, supports decision-making processes in O&M management, and further bridges the gap between human relationships with buildings/cities. This paper aims at going through the whole process of developing DTs in building and city levels from the technical perspective and sharing lessons learned and challenges involved in developing DTs in real practices. Through developing this DT demonstrator, the results provide a clear roadmap and present particular DT research efforts for asset management practitioners, policymakers, and researchers to promote the implementation and development of DT at the building and city levels

Generating real-valued failure data for prognostics under the conditions of limited Data Availability

—Data-driven prognostics solutions underperform under the conditions of limited failure data availability since the number of failure data samples is insufficient for training prognostics models effectively. In order to address this problem, we present a novel methodology for generating real-valued failure data which allows training datasets to be augmented so that the number of failure data samples is increased. In contrast to existing data generation techniques which duplicate or randomly generate data, the proposed methodology is capable of generating new and realistic failure data samples. To this end, we utilised the conditional generative adversarial network and auxiliary information pertaining to the failure modes. The proposed methodology is evaluated in a real-world case study involving the prediction of air purge valve failures in heavy trucks. Two prognostics models are developed using gradient boosting machine and random forest classifiers. It is shown that when these models are trained on the augmented training dataset, they outperform the best prognostics solution previously proposed in the literature for the case study by a large margin. More specifically, costs due to breakdowns and false alarms are reduced by 44%

Using expert knowledge to generate data for broadband line prognostics under limited failure data availability

Due to exposure to the driving rain, water ingress can cause faults in electrical joints, junctions and distribution points in broadband lines. Over time, faulting behaviour may grow in magnitude eroding the electrical capability of these lines causing degradation of broadband service. Developing effective data-driven models for broadband line prognostics remains a challenge due to the limited failure data availability in the telecommunications industry. In order to address this problem, we present a technique for generating failure data that realistically reflect the behaviour of degrading broadband lines. To this end, we use the conditional generative adversarial network and more importantly, we control and direct the failure data generation process using expert knowledge on the water ingress failure cause. The proposed technique is evaluated using a real-world case study involving the time-to-failure prediction of two types of broadband lines in a south-west city in England. The prognostics performance is measured using the Kappa statistic and F-score. Benchmark performance is obtained using Random Oversampling, Synthetic Minority Oversampling and Adaptive Synthesis which can be used to oversample failure data by duplicating existing failure data or randomly generating data. Among these techniques, Random Oversampling achieved the best prognostics performance. It is shown that the proposed technique outperforms Random Oversampling technique by a large margin. More specifically, it increased the prognostics performance by 33% (Kappa statistic) and 25% (F-score) for Asymmetric Digital Subscriber Lines, and 17% (Kappa statistic) and 13% (F-score) for Very High Bitrate Digital Subscriber Lines compared to the Random Oversampling technique

Using expert knowledge to generate data for broadband line prognostics under limited failure data availability

Due to exposure to the driving rain, water ingress can cause faults in electrical joints, junctions and distribution points in broadband lines. Over time, faulting behaviour may grow in magnitude eroding the electrical capability of these lines causing degradation of broadband service. Developing effective data-driven models for broadband line prognostics remains a challenge due to the limited failure data availability in the telecommunications industry. In order to address this problem, we present a technique for generating failure data that realistically reflect the behaviour of degrading broadband lines. To this end, we use the conditional generative adversarial network and more importantly, we control and direct the failure data generation process using expert knowledge on the water ingress failure cause. The proposed technique is evaluated using a real-world case study involving the time-to-failure prediction of two types of broadband lines in a south-west city in England. The prognostics performance is measured using the Kappa statistic and F-score. Benchmark performance is obtained using Random Oversampling, Synthetic Minority Oversampling and Adaptive Synthesis which can be used to oversample failure data by duplicating existing failure data or randomly generating data. Among these techniques, Random Oversampling achieved the best prognostics performance. It is shown that the proposed technique outperforms Random Oversampling technique by a large margin. More specifically, it increased the prognostics performance by 33% (Kappa statistic) and 25% (F-score) for Asymmetric Digital Subscriber Lines, and 17% (Kappa statistic) and 13% (F-score) for Very High Bitrate Digital Subscriber Lines compared to the Random Oversampling technique

Developing a dynamic digital twin at a building level: Using Cambridge campus as case study

A Digital Twin (DT) refers to a digital replica of physical assets, processes and systems. DTs integrate artificial intelligence, machine learning and data analytics to create dynamic digital models that are able to learn and update the status of the physical counterpart from multiple sources. A DT, if equipped with appropriate algorithms will represent and predict future condition and performance of their physical counterparts. Current developments related to DTs are still at an early stage with respect to buildings and other infrastructure assets. Most of these developments focus on the architectural and engineering/construction point of view. Less attention has been paid to the operation & maintenance (O&M) phase, where the value potential is immense. A systematic and clear architecture verified with practical use cases for constructing a DT is the foremost step for effective operation and maintenance of assets. This paper presents a system architecture for developing dynamic DTs in building levels for integrating heterogeneous data sources, support intelligent data query, and provide smarter decision-making processes. This will further bridge the gaps between human relationships with buildings/regions via a more intelligent, visual and sustainable channels. This architecture is brought to life through the development of a dynamic DT demonstrator of the West Cambridge site of the University of Cambridge. Specifically, this demonstrator integrates an as-is multi-layered IFC Building Information Model (BIM), building management system data, space management data, real-time Internet of Things (IoTj-based sensor data, asset registry data, and an asset tagging platform. The demonstrator also includes two applications: (1) improving asset maintenance and asset tracking using Augmented Reality (AR); and (2) equipment failure prediction. The long-term goals of this demonstrator are also discussed in this paper

Developing a dynamic digital twin at building and city levels: A case study of the West Cambridge campus

A Digital Twin (DT) refers to a digital replica of physical assets, processes and systems. DTs integrate artificial intelligence, machine learning and data analytics to create living digital simulation models that are able to learn and update from multiple sources, and to represent and predict the current and future conditions of physical counterparts. However, the current activities related to DTs are still at an early stage with respect to buildings and other infrastructure assets from an architectural and engineering/construction point of view. Less attention has been paid to the operation & maintenance (O&M) phase, which is the longest time span in the asset life cycle. A systematic and clear architecture verified with practical use cases for constructing a DT would be the foremost step for effective operation and maintenance of buildings and cities. According to current research about multi-tier architectures, this paper presents a system architecture for DTs which is specifically designed at both the building and city levels. Based on this architecture, a DT demonstrator of the West Cambridge site of the University of Cambridge was developed, which integrates heterogeneous data sources, supports effective data querying and analysing, supports decision-making processes in O&M management, and further bridges the gap between human relationships with buildings/cities. This paper aims at going through the whole process of developing DTs in building and city levels from the technical perspective and sharing lessons learnt and challenges involved in developing DTs in real practices. Through developing this DT demonstrator, the results provide a clear roadmap and present particular DT research efforts for asset management practitioners, policymakers and researchers to promote the implementation and development of DT at the building and city levels