Vehicle level health assessment through integrated operational scalable prognostic reasoners

Today’s aircraft are very complex in design and need constant monitoring of the systems to establish the overall health status. Integrated Vehicle Health Management (IVHM) is a major component in a new future asset management paradigm where a conscious effort is made to shift asset maintenance from a scheduled based approach to a more proactive and predictive approach. Its goal is to maximize asset operational availability while minimising downtime and the logistics footprint through monitoring deterioration of component conditions. IVHM involves data processing which comprehensively consists of capturing data related to assets, monitoring parameters, assessing current or future health conditions through prognostics and diagnostics engine and providing recommended maintenance actions. The data driven prognostics methods usually use a large amount of data to learn the degradation pattern (nominal model) and predict the future health. Usually the data which is run-to-failure used are accelerated data produced in lab environments, which is hardly the case in real life. Therefore, the nominal model is far from the present condition of the vehicle, hence the predictions will not be very accurate. The prediction model will try to follow the nominal models which mean more errors in the prediction, this is a major drawback of the data driven techniques. This research primarily presents the two novel techniques of adaptive data driven prognostics to capture the vehicle operational scalability degradation. Secondary the degradation information has been used as a Health index and in the Vehicle Level Reasoning System (VLRS). Novel VLRS are also presented in this research study. The research described here proposes a condition adaptive prognostics reasoning along with VLRS

Approaches for diagnosis and prognosis of asset condition: application to railway switch systems

This thesis presents a novel fault diagnosis and prognosis methodology which is applied to railway switches. To improve on existing fault diagnosis, energy-based thresholding wavelets (EBTW) are introduced. EBTW are used to decompose sensor measurement signals, and then to reconstruct them within a lower dimensional feature vector. The extracted features replace the original signals and are fed into a neural network classifier for fault diagnosis. Compared to existing wavelet-based feature extraction methods, the new EBTW method has the advantage of an intrinsic energy conservation property during the wavelet transform process. The EBTW method localises and redistributes the signal energy to realise an efficient feature extraction and dimension reduction. The presented diagnosis approach is validated using real-world switch data collected from the Guangzhou Metro in China. The results show that the proposed diagnosis approach can achieve 100% accuracy in identifying a railway switch overdriving fault with various severities, improving upon existing methods of conventional discrete wavelet transform (C-DWT) and soft-thresholding discrete wavelet transform (ST-DWT) by 8.33% and 16.67%, respectively. The presented prognosis approach is constructed based on traditional data-driven prognosis modelling. The concept of a remaining maintenance-free operating period (RMFOP) is introduced, which transforms the usefulness of sensor measurement data that is readily available from operations prior to failure. Useful features are then extracted from the original measurement data, and modelled using linear and exponential regression curve fitting models. By extracting key features, the original measurement data can be transformed into degradation signals that directly reflect the variations in each movement of a switch machine. The features are then fed into regression models to derive the probability distribution of switch residual life. To update the probability distribution from one operation to the next, Bayesian theory is incorporated into the models. The proposed RMFOP-based approach is validated using real-world electrical current sensor measurement data that were collected between January 2018 and February 2019 from multiple operational railway switches across Great Britain. The results show that the linear model and the exponential model can both provide residual life distributions with a satisfactory prediction accuracy. The exponential model demonstrates better predictions, the accuracy of which exceeds 95% when 90% life percentage has elapsed. By applying the RMFOP-based prognosis approach to operational data, the railway switch health condition that is affected by incipient overdriving failure is predicted

An investigation into the prognosis of electromagnetic relays.

Electrical contacts provide a well-proven solution to switching various loads in a wide variety of applications, such as power distribution, control applications, automotive and telecommunications. However, electrical contacts are known for limited reliability due to degradation effects upon the switching contacts due to arcing and fretting. Essentially, the life of the device may be determined by the limited life of the contacts. Failure to trip, spurious tripping and contact welding can, in critical applications such as control systems for avionics and nuclear power application, cause significant costs due to downtime, as well as safety implications. Prognostics provides a way to assess the remaining useful life (RUL) of a component based on its current state of health and its anticipated future usage and operating conditions. In this thesis, the effects of contact wear on a set of electromagnetic relays used in an avionic power controller is examined, and how contact resistance combined with a prognostic approach, can be used to ascertain the RUL of the device. Two methodologies are presented, firstly a Physics based Model (PbM) of the degradation using the predicted material loss due to arc damage. Secondly a computationally efficient technique using posterior degradation data to form a state space model in real time via a Sliding Window Recursive Least Squares (SWRLS) algorithm. Health monitoring using the presented techniques can provide knowledge of impending failure in high reliability applications where the risks associated with loss-of-functionality are too high to endure. The future states of the systems has been estimated based on a Particle and Kalman-filter projection of the models via a Bayesian framework. Performance of the prognostication health management algorithm during the contacts life has been quantified using performance evaluation metrics. Model predictions have been correlated with experimental data. Prognostic metrics including Prognostic Horizon (PH), alpha-Lamda (α-λ), and Relative Accuracy have been used to assess the performance of the damage proxies and a comparison of the two models made