Artificial Intelligence Supported EV Electric Powertrain for Safety Improvement

As an environmentally friendly transport option, electric vehicles (EVs) are endowed with the characteristics of low fossil energy consumption and low pollutant emissions. In today's growing market share of EVs, the safety and reliability of the powertrain system will be directly related to the safety of human life. Reliability problems of EV powertrains may occur in any power electronic (PE) component and mechanical part, both sudden and cumulative. These faults in different locations and degrees will continuously threaten the life of drivers and pedestrians, bringing irreparable consequences. Therefore, monitoring and predicting the real-time health status of EV powertrain is a high-priority, arduous and challenging task. The purposes of this study are to develop AI-supported effective safety improvement techniques for EV powertrains. In the first place, a literature review is carried out to illustrate the up-to-date AI applications for solving condition monitoring and fault detection issues of EV powertrains, where recent case studies between conventional methods and AI-based methods in EV applications are compared and analysed. On this ground this study, then, focuses on the theories and techniques concerning this topic so as to tackle different challenges encountered in the actual applications. In detail, first, as for diagnosing the bearing system in the earlier fault period, a novel inferable deep distilled attention network is designed to detect multiple bearing faults. Second, a deep learning and simulation driven approach that combines the domain-adversarial neural network and the lumped-parameter thermal network (LPTN) is proposed for achieve IPMSM permanent magnet temperature estimation work. Finally, to ensure the use safety of the IGBT module, deep learning -based IGBT modules’ double pulse test (DPT) efficiency enhancement is proposed and achieved via multimodal fusion networks and graph convolution networks

Eigen-spectrograms: an interpretable feature space for bearing fault diagnosis based on artificial intelligence and image processing

The Intelligent Fault Diagnosis of rotating machinery proposes some captivating challenges in light of the imminent big data era. Although results achieved by artificial intelligence and deep learning constantly improve, this field is characterized by several open issues. Models' interpretation is still buried under the foundations of data driven science, thus requiring attention to the development of new opportunities also for machine learning theories. This study proposes a machine learning diagnosis model, based on intelligent spectrogram recognition, via image processing. The approach is characterized by the introduction of the eigen-spectrograms and randomized linear algebra in fault diagnosis. The eigen-spectrograms hierarchically display inherent structures underlying spectrogram images. Also, different combinations of eigen-spectrograms are expected to describe multiple machine health states. Randomized algebra and eigen-spectrograms enable the construction of a significant feature space, which nonetheless emerges as a viable device to explore models' interpretations. The computational efficiency of randomized approaches further collocates this methodology in the big data perspective and provides new reading keys of well-established statistical learning theories, such as the Support Vector Machine (SVM). The conjunction of randomized algebra and Support Vector Machine for spectrogram recognition shows to be extremely accurate and efficient as compared to state of the art results.Comment: 14 pages, 13 figure

Reciprocating compressor prognostics of an instantaneous failure mode utilising temperature only measurements

Reciprocating compressors are critical components in the oil and gas sector, though their maintenance cost is known to be relatively high. Compressor valves are the weakest component, being the most frequent failure mode, accounting for almost half the maintenance cost. One of the major targets in industry is minimisation of downtime and cost, while maximising availability and safety of a machine, with maintenance considered a key aspect in achieving this objective. The concept of Condition Based Maintenance and Prognostics and Health Management (CBM/PHM) which is founded on the diagnostics and prognostics principles, is a step towards this direction as it offers a proactive means for scheduling maintenance. Despite the fact that diagnostics is an established area for reciprocating compressors, to date there is limited information in the open literature regarding prognostics, especially given the nature of failures can be instantaneous. This work presents an analysis of prognostic performance of several methods (multiple linear regression, polynomial regression, K-Nearest Neighbours Regression (KNNR)), in relation to their accuracy and variability, using actual temperature only valve failure data, an instantaneous failure mode, from an operating industrial compressor. Furthermore, a variation for Remaining Useful Life (RUL) estimation based on KNNR, along with an ensemble technique merging the results of all aforementioned methods are proposed. Prior to analysis, principal components analysis and statistical process control were employed to create !! and ! metrics, which were proposed to be used as health indicators reflecting degradation process of the valve failure mode and are proposed to be used for direct RUL estimation for the first time. Results demonstrated that even when RUL is relatively short due to instantaneous nature of failure mode, it is feasible to perform good RUL estimates using the proposed techniques

Ubiquitous Technologies for Emotion Recognition

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions