Predicting bridge elements deterioration, using Collaborative Gaussian Process Regression

Abstract: Roadway and railway bridges are not only integral, but also vulnerable parts of terrestrial transport networks. Structural failures of bridges may lead to disastrous consequences on users and society at large. Bridge predictive deterioration models are extremely important for effective maintenance decision-making. However, the lack of enough inspection data between maintenance activities of a bridge complicates the development of accurate predictive models. Presented herein is a Gaussian Process Regression (GPR) based collaborative model for predicting the condition of bridge elements with limited available inspection data per bridge. This model has been applied in 137 bridge decks, showing that collaborative prognosis has the potential to predict the condition of different types of bridge elements, composing different types of bridges.This work was supported by the European Community’s H2020 Programme MG7-1-2017 Resilience to extreme (natural and man- made) events [grant number 769255] - “GIS-based infrastructure management system for optimised response to extreme events of terrestrial transport networks (SAFEWAY)

Empirical convergence analysis of federated averaging for failure prognosis

Data driven prognosis involves machine learning algorithms to learn from previous failures and generate its prediction model. However, often a single asset does not fail so frequently to have enough training data in the form of historical failures. This problem can be addressed by learning from failures across a cluster of similar other assets, but often working in different environments. The algorithm therefore must learn from a distributed dataset which might be heterogenous but with underlying similarities. Federated Learning is an emerging technique that has recently also been proposed as a fitting solution for prognosis of industrial assets. However, even the most commonly used Federated Learning algorithms lack theoretical convergence guarantees, and therefore their convergence must be analysed empirically. This paper empirically analyses the convergence of the Federated Averaging (FedAvg) algorithm for a fleet of simulated turbofan engines. Results demonstrate that while FedAvg is applicable for prognosis, it cannot acknowledge the differences in asset failure mechanisms. As a result, the prognosis framework needs to be modified such that similar failures are clustered together before FedAvg can be implemented.This research was funded by the EPSRC and BT Prosperity Part- nership project: Next Generation Converged Digital Infrastructure, grant number EP/R004935/

Product quality driven auto-prognostics: Low-cost digital solution for SMEs

Setting out existing prognostics solutions in small and medium enterprises (SMEs) is accompanied by challenges. These include employing expensive sensors, acquisition systems; and attending geometric limitations. Additionally, these solutions call for a specialist to take on feature engineering, machine learning algorithm selection, etc. Presented in this paper is a low-cost digital solution (intelligently integrate cost-cutting off-the-shelf technologies) for SMEs via product quality driven auto-prognostics. First, we develop upon existing solutions by addressing their drawbacks viz. cost, geometric limitations via a new product quality-centered condition monitoring strategy. Every SME must investigate the quality of their products, and therefore the authors believe this to be a low-cost solution. Next, the proposed solution integrates automated machine learning via Auto-WEKA, an off-the-shelf open-source technology. Lastly, the practical advantages of the proposed solution over the existing sensor-based solution were investigated via a case study. Results depict that this low-cost prognostics solution is vital for maintenance planning in SMEs.Royal Academy of Engineering London, UK (IAPP 18-19/31)

Comparison of Agent Deployment Strategies for Collaborative Prognosis

Collaborative prognosis is a technique that enables the industrial assets to learn from similar other assets in a fleet, and improve their data-driven prognosis models. When collabo- rative prognosis is implemented in a computationally distributed framework, each asset is monitored by its corresponding Digital Twin agent. Distributed collaborative prognosis is particularly beneficial for high value assets where the communication and the processing costs are negligible compared to the maintenance costs. This paper analyses the effects of Digital Twin deployment strategies on the effectiveness of predictive maintenance activities relying on distributed collaborative prognosis. Distributed and heterarchical multi-agent system architectures are analysed for large fleets of assets, with varying failure rates and noise levels in the failure data. The results show that no single architecture or deployment strategy can be deemed best across all failure rates and noise levels. The conclusion derived in this paper provides guidance to the asset owners to choose the most suitable combination for a given application.Next Generation Converged Digital Infrastructure project (EP/R004935/1) funded by the Engineering and Physical Sciences Research Council and B

Secure and communications-efficient collaborative prognosis

Collaborative prognosis is a technique that is used to enable assets to improve their ability to predict failures by learning from the failures of similar other assets. This is typically made possible by enabling the assets to communicate with each other. The key enabler of current collaborative prognosis techniques is that they require assets to share their sensor data and failure information between each other, which might be a major constraint due to commercial sensitivities, especially when the assets belong to different companies. This paper uses Federated Learning to address this issue, and examines whether this technique will enable collaborative prognosis while ensuring sensitive operational data is not shared between organisational boundaries. An example implementation is demonstrated for prognosis of a simulated turbofan fleet, where Federated Averaging algorithm is used as an alternative for the data exchange step. Its performance is compared with conventional collaborative prognosis that involves failure data exchange. The results confirm that Federated Averaging retains the performance of conventional collaborative prognosis, while eliminating the exchange of failure data within assets. This removes a critical hinderance in industrial adoption of collaborative prognosis, thus enhancing the potential of predictive maintenance

Anomaly detection in a fleet of industrial assets with hierarchical statistical modeling

Abstract Anomaly detection in asset condition data is critical for reliable industrial asset operations. But statistical anomaly classifiers require certain amount of normal operations training data before acceptable accuracy can be achieved. The necessary training data are often not available in the early periods of assets operations. This problem is addressed in this paper using a hierarchical model for the asset fleet that systematically identifies similar assets, and enables collaborative learning within the clusters of similar assets. The general behavior of the similar assets are represented using higher level models, from which the parameters are sampled describing the individual asset operations. Hierarchical models enable the individuals from a population, comprising of statistically coherent subpopulations, to collaboratively learn from one another. Results obtained with the hierarchical model show a marked improvement in anomaly detection for assets having low amount of data, compared to independent modeling or having a model common to the entire fleet.This research was funded by the EPSRC and BT Prosperity Partnership project: Next Generation Converged Digital Infrastructure, grant number EP/R004935/

Hierarchical Bayesian modeling for knowledge transfer across engineering fleets via multitask learning

A population-level analysis is proposed to address data sparsity when building predictive models for engineering infrastructure. Utilizing an interpretable hierarchical Bayesian approach and operational fleet data, domain expertise is naturally encoded (and appropriately shared) between different subgroups, representing (1) use-type, (2) component, or (3) operating condition. Specifically, domain expertise is exploited to constrain the model via assumptions (and prior distributions) allowing the methodology to automatically share information between similar assets, improving the survival analysis of a truck fleet (15% and 13% increases in predictive log-likelihood of hazard) and power prediction in a wind farm (up to 82% reduction in the standard deviation of maximum output prediction). In each asset management example, a set of correlated functions is learnt over the fleet, in a combined inference, to learn a population model. Parameter estimation is improved when subfleets are allowed to share correlated information at different levels in the hierarchy; the (averaged) reduction in standard deviation for interpretable parameters in the survival analysis is 70%, alongside 32% in wind farm power models. In turn, groups with incomplete data automatically borrow statistical strength from those that are data-rich. The statistical correlations enable knowledge transfer via Bayesian transfer learning, and the correlations can be inspected to inform which assets share information for which effect (i.e., parameter). Successes in both case studies demonstrate the wide applicability in practical infrastructure monitoring, since the approach is naturally adapted between interpretable fleet models of different in situ examples

Genomic epidemiology of SARS-CoV-2 in a UK university identifies dynamics of transmission

AbstractUnderstanding SARS-CoV-2 transmission in higher education settings is important to limit spread between students, and into at-risk populations. In this study, we sequenced 482 SARS-CoV-2 isolates from the University of Cambridge from 5 October to 6 December 2020. We perform a detailed phylogenetic comparison with 972 isolates from the surrounding community, complemented with epidemiological and contact tracing data, to determine transmission dynamics. We observe limited viral introductions into the university; the majority of student cases were linked to a single genetic cluster, likely following social gatherings at a venue outside the university. We identify considerable onward transmission associated with student accommodation and courses; this was effectively contained using local infection control measures and following a national lockdown. Transmission clusters were largely segregated within the university or the community. Our study highlights key determinants of SARS-CoV-2 transmission and effective interventions in a higher education setting that will inform public health policy during pandemics.</jats:p

Predicting bridge elements deterioration, using Collaborative Gaussian Process Regression

Roadway and railway bridges are not only integral, but also vulnerable parts of terrestrial transport networks. Structural failures of bridges may lead to disastrous consequences on users and society at large. Bridge predictive deterioration models are extremely important for effective maintenance decision-making. However, the lack of enough inspection data between maintenance activities of a bridge complicates the development of accurate predictive models. Presented herein is a Gaussian Process Regression (GPR) based collaborative model for predicting the condition of bridge elements with limited available inspection data per bridge. This model has been applied in 137 bridge decks, showing that collaborative prognosis has the potential to predict the condition of different types of bridge elements, composing different types of bridges

Empirical convergence analysis of federated averaging for failure prognosis

Data driven prognosis involves machine learning algorithms to learn from previous failures and generate its prediction model. However, often a single asset does not fail so frequently to have enough training data in the form of historical failures. This problem can be addressed by learning from failures across a cluster of similar other assets, but often working in different environments. The algorithm therefore must learn from a distributed dataset which might be heterogenous but with underlying similarities. Federated Learning is an emerging technique that has recently also been proposed as a fitting solution for prognosis of industrial assets. However, even the most commonly used Federated Learning algorithms lack theoretical convergence guarantees, and therefore their convergence must be analysed empirically. This paper empirically analyses the convergence of the Federated Averaging (FedAvg) algorithm for a fleet of simulated turbofan engines. Results demonstrate that while FedAvg is applicable for prognosis, it cannot acknowledge the differences in asset failure mechanisms. As a result, the prognosis framework needs to be modified such that similar failures are clustered together before FedAvg can be implemented