This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

A comprehensive review on convolutional neural network in machine fault diagnosis

Deep learning for prognostics and health management: State of the art, challenges, and opportunities

Normal operation of bearing is the key to ensure the reliability and security of rotary machinery, so that bearing fault diagnosis is quite significant. However, the large amount of data collected by modern data acquisition system and time-varying working conditions make it hard to diagnose the fault using traditional methods To break the predicaments, we propose a new intelligent fault diagnosis framework inspired by the infinitesimal method. The proposed model including three parts can ignore the effect of different rotational speeds. Firstly, the sample is segmented and every segment dimension is extended by input network to ensure the adequate information memory space. Secondly, the classification information is stored and transferred in the long short-term memory (LSTM) network and output to the third part. In this process, the working condition information is ignored because of the gate units function. Finally, the likelihood is given by output network to classify the health conditions. Besides, we propose a loss function combining all the output of every time step and employ dropout to train the model, which increase the training efficiency and diagnosis ability. The bearing datasets under time-varying speeds and loads are used to verify the proposed method. The application result shows that our method has higher accuracy with simpler structure, and is superior to the traditional method in bearing fault diagnosis. Moreover, we give a physical interpretation of the proposed model.

A novel bearing intelligent fault diagnosis framework under time-varying working conditions using recurrent neural network.

A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles

A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries

The diagnosis of the key components of rotating machinery systems is essential for the production efficiency and quality of manufacturing processes. The performance of the traditional diagnosis method depends heavily on feature extraction, which relies on the degree of individual's expertise or prior knowledge. Recently, a deep learning (DL) method is applied to automate feature extraction. However, training in the DL method requires a massive amount of sensor data, which is time consuming and poses a challenge for its applications in engineering. In this paper, a new data-driven fault diagnosis method based on compressed sensing (CS) and improved multiscale network (IMSN) is proposed to recognize and classify the faults in rotating machinery. CS is used to reduce the amount of raw data, from which the fault information is discovered. At the same time, it can be used to generate sufficient training samples for the subsequent learning. The one-dimensional compressed signal is converted to two-dimensional image for further learning. An IMSN is established for learning and obtaining deep features. It improves the diagnosis performance of the DL process. The faults of the key components are identified from a softmax model. Experimental analysis is performed to verify effectiveness of the proposed data-driven fault diagnosis method.

Data-Driven Fault Diagnosis Method Based on Compressed Sensing and Improved Multiscale Network

Vibration signals always contain noise and irregularities which makes spectrum analysis difficult to extract high-level features. Recently, graph theory has been applied to spectrum analysis to improve the performance of feature extraction. By converting the raw data into graphs, hidden structural and topological information can be obtained. In this paper, a spatial-temporal graph-based feature extraction, called SuperGraph, for rotating machinery fault diagnosis is proposed. Specifically, graph theory-based spectrum analysis is used to construct the spatial-temporal graph. Then, the Laplacian matrix-based feature vector is extracted from the constructed spatial-temporal graph. By this means, the spatial-temporal graph is converted into the one-dimensional vector for further constructing SuperGraph, where each node of the SuperGraph represents a spatial-temporal graph and the SuperGraph is composed of many local graphs. In the local graph, only the same type of nodes are connected to form a fully connected graph. Thus, the task of graph classification can be transformed into classifying the nodes in the SuperGraph. After graph convolutional network is established for learning and obtaining deep features, the label of nodes is identified from a softmax model. Experiments are conducted on two benchmarking datasets and a practical experimental platform to verify effectiveness of the proposed fau

Jie Liu

Papers

A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries

Data-Driven Fault Diagnosis Method Based on Compressed Sensing and Improved Multiscale Network

SuperGraph: Spatial-temporal graph-based feature extraction for rotating machinery diagnosis

A CRNN module for hand pose estimation

Deep Convolutional Neural Network-based Bernoulli Heatmap for Head Pose Estimation