A review on artificial intelligence in high-speed rail

High-speed rail (HSR) has brought a number of social and economic benefits, such as shorter trip times for journeys of between one and five hours; safety, security, comfort and on-time commuting for passengers; energy saving and environmental protection; job creation; and encouraging sustainable use of renewable energy and land. The recent development in HSR has seen the pervasive applications of artificial intelligence (AI). This paper first briefly reviews the related disciplines in HSR where AI may play an important role, such as civil engineering, mechanical engineering, electrical engineering and signalling and control. Then, an overview of current AI techniques is presented in the context of smart planning, intelligent control and intelligent maintenance of HSR systems. Finally, a framework of future HSR systems where AI is expected to play a key role is provided

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Fault data seasonal imbalance and insufficiency impacts on data-driven heating, ventilation and air-conditioning fault detection and diagnosis performances for energy-efficient building operations

The heating, ventilation and air-conditioning fault impacts vary with different seasonal climatic conditions, but the fault data may not be available under some seasons in real buildings due to the frequency and span of fault occurrences. This study evaluates the fault detection and diagnosis (FDD) performance differences of the proposed convolutional and recurrent neural networks under limited seasonal fault data scenarios and an ideal scenario covering climatic conditions from multiple seasons. The fault and normal data were gathered from fault simulations using a verified prototype building EnergyPlus model and two real fault datasets. Four different data experiments based on the simulated dataset were implemented to assess FDD performance differences, and two sets of further experiments based on each real fault dataset were conducted to verify the findings from previous experiments. The results show that the FDD architectures, trained on sufficient fault data under a certain season(s), indicate poor generalization ability to identify faults under unseen seasons. Moreover, the coverage of fault data under different seasons is more crucial in enhancing FDD performances than the amount of fault data under each season. These findings will help researchers consider this practical issue when evaluating new or existing data-driven FDD methods

Novel deep cross-domain framework for fault diagnosis or rotary machinery in prognostics and health management

Improving the reliability of engineered systems is a crucial problem in many applications in various engineering fields, such as aerospace, nuclear energy, and water declination industries. This requires efficient and effective system health monitoring methods, including processing and analyzing massive machinery data to detect anomalies and performing diagnosis and prognosis. In recent years, deep learning has been a fast-growing field and has shown promising results for Prognostics and Health Management (PHM) in interpreting condition monitoring signals such as vibration, acoustic emission, and pressure due to its capacity to mine complex representations from raw data. This doctoral research provides a systematic review of state-of-the-art deep learning-based PHM frameworks, an empirical analysis on bearing fault diagnosis benchmarks, and a novel multi-source domain adaptation framework. It emphasizes the most recent trends within the field and presents the benefits and potentials of state-of-the-art deep neural networks for system health management. Besides, the limitations and challenges of the existing technologies are discussed, which leads to opportunities for future research. The empirical study of the benchmarks highlights the evaluation results of the existing models on bearing fault diagnosis benchmark datasets in terms of various performance metrics such as accuracy and training time. The result of the study is very important for comparing or testing new models. A novel multi-source domain adaptation framework for fault diagnosis of rotary machinery is also proposed, which aligns the domains in both feature-level and task-level. The proposed framework transfers the knowledge from multiple labeled source domains into a single unlabeled target domain by reducing the feature distribution discrepancy between the target domain and each source domain. Besides, the model can be easily reduced to a single-source domain adaptation problem. Also, the model can be readily updated to unsupervised domain adaptation problems in other fields such as image classification and image segmentation. Further, the proposed model is modified with a novel conditional weighting mechanism that aligns the class-conditional probability of the domains and reduces the effect of irrelevant source domain which is a critical issue in multi-source domain adaptation algorithms. The experimental verification results show the superiority of the proposed framework over state-of-the-art multi-source domain-adaptation models

Advanced Sensors for Real-Time Monitoring Applications

It is impossible to imagine the modern world without sensors, or without real-time information about almost everything—from local temperature to material composition and health parameters. We sense, measure, and process data and act accordingly all the time. In fact, real-time monitoring and information is key to a successful business, an assistant in life-saving decisions that healthcare professionals make, and a tool in research that could revolutionize the future. To ensure that sensors address the rapidly developing needs of various areas of our lives and activities, scientists, researchers, manufacturers, and end-users have established an efficient dialogue so that the newest technological achievements in all aspects of real-time sensing can be implemented for the benefit of the wider community. This book documents some of the results of such a dialogue and reports on advances in sensors and sensor systems for existing and emerging real-time monitoring applications

Fault diagnosis of a wave energy converter gearbox based on an Adam optimized CNN-LSTM algorithm

The complex structure and harsh operating environment of wave energy converters can result in various faults in transmission components. Environmental noise in practical operating situations may obscure the effective information in collected vibration signals, significantly increasing the difficulty of fault diagnosis. This paper presents a fault diagnosis model for the gearbox of the point absorber wave energy converter. The model integrates a convolutional neural network with long short-term memory to realize efficient extraction of local features from signals and enhance the performance in time-series analysis. Moreover, the model incorporates the Adaptive Moment Estimation algorithm to address the situations where gradients within tensors exhibit unstable changes in the model. A rigid-flexible coupled dynamics simulation model is developed to simulate vibration signals used to train and verify the fault diagnosis model. Experimental tests of the proposed model on a vibration dataset acquired from real vibration experiments demonstrate its efficacy in diagnosing various types of faults under interference of operating conditions. Comparative studies with other models demonstrate the superiority of the proposed model in terms of fault feature extraction, learning convergence efficiency, and diagnostic accuracy, indicating that the proposed model can achieve faster and more accurate fault diagnosis of wave energy converter gearboxes

UNSUPERVISED PROBABILISTIC ANOMALY DETECTION OVER NOMINAL SUBSYSTEM EVENTS THROUGH A HIERARCHICAL VARIATIONAL AUTOENCODER

This work develops a versatile approach to discover anomalies in operational data for nominal (i.e., non-parametric) subsystem event signals using unsupervised Deep Learning techniques. Firstly, it builds a neural convolutional framework to extract both intrasubsystem and intersubsystem patterns. This is done by applying banks of voxel filters on the charted data. Secondly, it generalizes the learned embedded regularity of a Variational Autoencoder manifold by merging latent space-overlapping deviations with non-overlapping synthetic irregularities. Contingencies like novel data, model drift, etc., are therefore seamlessly managed by the proposed data-augmented approach. Finally, it creates a smooth diagnosis probabilistic function on the ensuing low-dimensional distributed representation. The resulting enhanced solution warrants analytically strong tools for a critical industrial environment. It also facilitates its hierarchical integrability, and provides visually interpretable insights of the degraded condition hazard to increase the confidence in its predictions. This strategy has been validated with eight pairwise-interrelated subsystems from high-speed trains. Its outcome also leads to further reliable explainability from a causal perspective

Explainable fault prediction using learning fuzzy cognitive maps

IoT sensors capture different aspects of the environment and generate high throughput data streams. Besides capturing these data streams and reporting the monitoring information, there is significant potential for adopting deep learning to identify valuable insights for predictive preventive maintenance. One specific class of applications involves using Long Short-Term Memory Networks (LSTMs) to predict faults happening in the near future. However, despite their remarkable performance, LSTMs can be very opaque. This paper deals with this issue by applying Learning Fuzzy Cognitive Maps (LFCMs) for developing simplified auxiliary models that can provide greater transparency. An LSTM model for predicting faults of industrial bearings based on readings from vibration sensors is developed to evaluate the idea. An LFCM is then used to imitate the performance of the baseline LSTM model. Through static and dynamic analyses, we demonstrate that LFCM can highlight (i) which members in a sequence of readings contribute to the prediction result and (ii) which values could be controlled to prevent possible faults. Moreover, we compare LFCM with state-of-the-art methods reported in the literature, including decision trees and SHAP values. The experiments show that LFCM offers some advantages over these methods. Moreover, LFCM, by conducting a what-if analysis, could provide more information about the black-box model. To the best of our knowledge, this is the first time LFCMs have been used to simplify a deep learning model to offer greater explainability