Structural health monitoring of a footbridge using Echo State Networks and NARMAX

Echo State Networks (ESNs) and a Nonlinear Auto-Regressive Moving Average model with eXogenous inputs (NARMAX) have been applied to multi-sensor time-series data arising from a test footbridge which has been subjected to multiple potentially damaging interventions. The aim of the work was to automatically classify known potentially damaging events, while also allowing engineers to observe and localise any long term damage trends. The techniques reported here used data from ten temperature sensors as inputs and were tasked with predicting the output signal from eight tilt sensors embedded at various points over the bridge. Initially, interventions were identified by both ESNs and NARMAX. In addition, training ESNs using data up to the first event, and determining the ESNs’ subsequent predictions, allowed inferences to be made not only about when and where the interventions occurred, but also the level of damage caused, without requiring any prior data pre-processing or extrapolation. Finally, ESNs were successfully used as classifiers to characterise various different types of intervention that had taken place

Fusion of heterogeneous data in non-destructive testing and structural health monitoring using Echo State Networks

Failure to monitor the condition of key infrastructure such as roads and bridges can result in costly closures, but the economic impact could be lessened by early intervention. Non-destructive testing (NDT) examines structures without causing damage, while structural health monitoring (SHM) monitors a structure throughout its life. This thesis presents a machine learning approach to fusing heterogeneous sensor modalities that can be systematically applied to improve sensor interpretation and reduce reliance on expertise. For the first time, echo state networks (ESNs) were used in two separate NDT and SHM data fusion case studies. The NDT-based study looked at detecting defects in steel reinforcement, teaching ESNs to combine magnetic flux leakage (MFL) and cover depth data in order to compensate for variation in MFL amplitude with increasing cover depth. Using seven different cover depths between 42.5 mm and 289 mm, the fusion approach offered improved performance for 42.5mm < depth < 205mm and the most consistent calculated optimal output threshold, demonstrating the ease of systematic application. In the SHM-based study, data from the National Physical Laboratory (NPL) footbridge monitoring project was processed by a suite of ESNs to detect, localise, classify and assess damage caused by deliberate interventions. A novel approach of combining physical and environmental sensors in order to model a different modality of physical sensor made it possible to use the residual to observe damage trends and locations, which also led to the isolation of a faulty strain gauge. There was additional success in distinguishing between different intervention types and producing a metric to express the damage level. Across both studies, the ESN approach to heterogeneous data fusion improved upon non-fusion-based alternatives. This suggests that future work should consider structures that are in regular use, combining further sensor modalities and the development of bespoke data fusion software

Hybrid simulation techniques in the structural analysis and testing of architectural heritage

L'abstract è presente nell'allegato / the abstract is in the attachmen

Canonical variable analysis and long short-term memory for fault diagnosis and performance estimation of a centrifugal compressor

© 2017 Centrifugal compressors are widely used for gas lift, re-injection and transport in the oil and gas industry. Critical compressors that compress flammable gases and operate at high speeds are prioritized on maintenance lists to minimize safety risks and operational downtime hazards. Identifying incipient faults and predicting fault evolution for centrifugal compressors could improve plant safety and efficiency and reduce maintenance and operation costs. This study proposes a dynamic process monitoring method based on canonical variable analysis (CVA) and long short-term memory (LSTM). CVA was used to perform fault detection and identification based on the abnormalities in the canonical state and the residual space. In addition, CVA combined with LSTM was used to estimate the behavior of a system after the occurrence of a fault using data captured from the early stages of deterioration. The approach was evaluated using process data obtained from an operational industrial centrifugal compressor. The results show that the proposed method can effectively detect process abnormalities and perform multi-step-ahead prediction of the system's behavior after the appearance of a fault

Model Order Reduction

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This three-volume handbook covers methods as well as applications. This third volume focuses on applications in engineering, biomedical engineering, computational physics and computer science