Fault Diagnosis and Failure Prognostics of Lithium-ion Battery based on Least Squares Support Vector Machine and Memory Particle Filter Framework

123456A novel data driven approach is developed for fault diagnosis and remaining useful life (RUL) prognostics for lithium-ion batteries using Least Square Support Vector Machine (LS-SVM) and Memory-Particle Filter (M-PF). Unlike traditional data-driven models for capacity fault diagnosis and failure prognosis, which require multidimensional physical characteristics, the proposed algorithm uses only two variables: Energy Efficiency (EE), and Work Temperature. The aim of this novel framework is to improve the accuracy of incipient and abrupt faults diagnosis and failure prognosis. First, the LSSVM is used to generate residual signal based on capacity fade trends of the Li-ion batteries. Second, adaptive threshold model is developed based on several factors including input, output model error, disturbance, and drift parameter. The adaptive threshold is used to tackle the shortcoming of a fixed threshold. Third, the M-PF is proposed as the new method for failure prognostic to determine Remaining Useful Life (RUL). The M-PF is based on the assumption of the availability of real-time observation and historical data, where the historical failure data can be used instead of the physical failure model within the particle filter. The feasibility of the framework is validated using Li-ion battery prognostic data obtained from the National Aeronautic and Space Administration (NASA) Ames Prognostic Center of Excellence (PCoE). The experimental results show the following: (1) fewer data dimensions for the input data are required compared to traditional empirical models; (2) the proposed diagnostic approach provides an effective way of diagnosing Li-ion battery fault; (3) the proposed prognostic approach can predict the RUL of Li-ion batteries with small error, and has high prediction accuracy; and, (4) the proposed prognostic approach shows that historical failure data can be used instead of a physical failure model in the particle filter

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Advanced uncertainty quantification with dynamic prediction techniques under limited data for industrial maintenance applications.

Zhao, Yifan - Associate supervisorEngineering systems are expected to function effectively whilst maintaining reliability in service. These systems consist of various equipment units, many of which are maintained on a corrective or time-based basis. Challenges to plan maintenance accounting for turnaround times, equipment availability and resulting costs manifest varying degrees of uncertainty stemming from multiple quantitative and qualitative (compound) sources throughout the in-service life. Under or over-estimating this uncertainty can lead to increased failure rates or, more often, unnecessary maintenance being carried out. As well as the quality availability of data, uncertainty is driven by the influence of expert experience or assumptions and environmental operating conditions. Accommodating for uncertainty requires the determination of key contributors, their influence on interconnected units and how this might change over time. This research aims to develop a modelling approach to quantify, aggregate and forecast uncertainty given by a combination of historic equipment data and heuristic estimates for in-service engineering systems. Research gaps and challenges are identified through a systematic literature review and supported by a series of surveys and interviews with industrial practitioners. These are addressed by the development of two frameworks: (1) quantify and aggregate compound uncertainty, and (2) predict uncertainty under limited data. The two frameworks are brought together to produce the Multistep Compound Dynamic Uncertainty Quantification (MCDUQ) app, developed in MATLAB. Results demonstrate effective measurement of compound uncertainties and their impact on system reliability, along with robust predictions under limited data with an immersive visualisation of dynamic uncertainty. The embedded frameworks are each validated through implementation in two case studies. The app is verified with industrial experts through a series of interviews and virtual demonstrations.PhD in Manufacturin