This review paper is to give a full picture of fault detection and diagnosis (FDD) in complex systems from the perspective of data processing. As a matter of fact, an FDD system is a data-processing ...system on the basis of information redundancy, in which the data and human's understanding of the data are two fundamental elements. Human's understanding may be an explicit input-output model representing the relationship among the system's variables. It may also be represented as knowledge implicitly (e.g., the connection weights of a neural network). Therefore, FDD is done through some kind of modeling, signal processing, and intelligence computation. In this paper, a variety of FDD techniques are reviewed within the unified data-processing framework to give a full picture of FDD and achieve a new level of understanding. According to the types of data and how the data are processed, the FDD methods are classified into three categories: model-based online data-driven methods, signal-based methods, and knowledge-based history data-driven methods. An outlook to the possible evolution of FDD in industrial automation, including the hybrid FDD and the emerging networked FDD, are also presented to reveal the future development direction in this field.
In this paper, for the safe and efficient cooperation of high-speed trains, a sliding-mode adaptive control strategy is investigated for cooperative control of trains subject to state constraints and ...input saturation. Firstly, in order to guarantee the safety of inter-train distance and over-speed protection, the position and speed limitations which could be seen as state constraints are integrated as the hybrid prescribed performance. Then, a sliding-mode surface is selected and a sliding-mode adaptive control protocol is designed to meet the input saturation and hybrid prescribed performance for the multi-train systems. Afterwards, a Barrier Lyapunov Function is designed to prove the stability of multi-train systems subject to state constraints and input saturation. Using the sliding-mode adaptive control and hybrid prescribed performance control methods, the safe and efficient operation of trains can be realized. Finally, the effectiveness of proposed method is given by the simulation results.
Precise timing plays a key role in the time‐sensitive industrial Internet of Things (IIoT). However, precise time synchronization requires more frequent packet exchange, which consumes more ...communication bandwidth and energy. This is a particular challenge in battery‐powered wireless nodes, and low communication costs have become an important factor in clock synchronization. To address the challenge of achieving low communication cost clock synchronization in distributed wireless sensor networks, this paper proposes an improved event‐triggered control and synchronization scheme with a novel asynchronous broadcast packet exchange protocol. Unlike the traditional event‐triggered control scheme which is based on synchronous polling packet exchange, this proposed asynchronous broadcast packet exchange is more communication efficient and requires fewer number of packet exchanges. And it is worth noting that the proposed algorithm in this paper is a distributed algorithm and does not require real‐time acquisition of information from neighbouring nodes. Finally, a numerical example is given to illustrate the effectiveness of the proposed event‐triggered control strategy. The efficiency and precision of the proposed clock synchronization method is evaluated by intensive simulations, which show that the number of packet exchange is reduced by 60% for a moderate IIoT network and is particularly useful for large scale network.
Different from many existed event‐triggered communication mechanisms, the event‐triggered mechanism proposed in this paper adopts an asynchronous broadcast packets strategy, in which only the local trigger will broadcast packets and achieve a better saving of communication resources consumption. And numerical example is given to illustrate the effectiveness of the proposed event‐triggered control strategy.
In this paper technique is employed in a Brillouin optical time domain reflectometry (BOTDR) using a Brillouin ring laser (BRL) as a local oscillator. In the wavelength diversity technique, multiple ...wavelengths are injected into the sensing fiber, while the peak power of each wavelength is set below the nonlinear threshold level. This technique significantly maximizes the overall launch pump power, without activating the nonnegligible nonlinear effects, which overcomes the limitation of the conventional BOTDR system. The BRL, which is simple and cost-effective, that can be used to reduce the receiver bandwidth in the order of few MHz. In addition, a passive depolarizer is used to reduce the polarization noise. The proposed system is validated experimentally over a 50 km sensing fiber with a 5 m spatial resolution. The experimental results demonstrate a signal-to-noise ratio improvement of 5.1 dB, which corresponds to 180% improvement compared to a conventional BOTDR system.
In many Wireless Sensor Network (WSN) applications, the availability of a simple yet accurate estimation of the RF channel quality is vital. However, due to measurement noise and fading effects, it ...is usually estimated through probe or learning based methods, which result in high energy consumption or high overheads. We propose to make use of information redundancy among indicators provided by the IEEE 802.15.4 system to improve the estimation of the link quality. A Kalman filter based solution is used due to its ability to give an accurate estimate of the un-measurable states of a dynamic system subject to observation noise. In this paper we present an empirical study showing that an improved indicator, termed Effective-SNR, can be produced by combining Signal to Noise Ratio (SNR) and Link Quality Indicator (LQI) with minimal additional overhead. The estimation accuracy is further improved through the use of Kalman filtering techniques. Finally, experimental results demonstrate that the proposed algorithm can be implemented on resource constraints devices typical in WSNs.
This paper investigates the interaction phenomena of the coupled axons while the mutual coupling factor is presented as a pairwise description. Based on the Hodgkin-Huxley model and the coupling ...factor matrix, the membrane potentials of the coupled myelinated/unmyelinated axons are quantified which implies that the neural coupling can be characterised by the presented coupling factor. Meanwhile the equivalent electric circuit is supplied to illustrate the physical meaning of this extended model. In order to estimate the coupling factor, a data-based iterative learning identification algorithm is presented where the Rényi entropy of the estimation error has been minimised. The convergence of the presented algorithm is analysed and the learning rate is designed. To verified the presented model and the algorithm, the numerical simulation results indicate the correctness and the effectiveness. Furthermore, the statistical description of the neural coupling, the approximation using ordinary differential equation, the measurement and the conduction of the nerve signals are discussed respectively as advanced topics. The novelties can be summarised as follows: 1) the Hodgkin-Huxley model has been extended considering the mutual interaction between the neural axon membranes, 2) the iterative learning approach has been developed for factor identification using entropy criterion, and 3) the theoretical framework has been established for this class of system identification problems with convergence analysis.
In this paper, we propose and experimentally demonstrate for the first time, the integration of a radio-over-fiber (RoF) communication system and a Brillouin optical time-domain reflectometry (BOTDR) ...distributed sensor system using a single optical fiber link. In this proof-of-concept integrated system, the communication system is composed of three modulation formats of quadrature phase-shift keying (QPSK), 16-quadrature amplitude modulation (16-QAM) and 64-QAM, which are modulated onto an orthogonal frequency division multiplexing (OFDM) signal. Whereas, the BOTDR sensor system is used for strain and/or temperature monitoring over the fiber distance with a spatial resolution of 5 m using a 25 km single-mode silica fiber. The error vector magnitude (EVM) is analyzed in three modulation formats in the presence of various BOTDR input pump powers. Using QPSK modulation, optimized 18 dBm sensing and 10 dBm data power, the measured EVM values with and without bandpass filter are 3.5% and 14.5%, respectively. The proposed system demonstrates a low temperature measurement error (±0.49 °C at the end of 25 km) and acceptable EVM values, which were within the 3GPP requirements. The proposed integrated system can be effectively applied for practical applications, which significantly reduces the fiber infrastructure cost by effective usage of a single optical fiber link.
Cyber-physical systems (CPSs) are characterized by integrating computation, communication, and physical system. In typical CPS application scenarios, vehicle-to-vehicle (V2V) and Industry Internet of ...Things (IIoT), due to doubly selective fading and non-stationary channel characteristics, the robust and reliable end-to-end communication is extremely important. Channel estimation is a major signal processing technology to ensure robust and reliable communication. However, the existing channel estimation methods for V2V and IIoT cannot effectively reduce intercarrier interference (ICI) and lower the computation complexity, thus leading to poor robustness. Aiming at this challenge, according to the channel characteristics of V2V and IIoT, we design two channel estimation methods based on the Bayesian filter to promote the robustness and reliability of end-to-end communication. For the channels with doubly selective fading and non-stationary characteristics of V2V and IIoT scenarios, in the one hand, basis extended model (BEM) is used to further reduce the complexity of the channel estimation algorithm under the premise that ICI can be eliminated in the channel estimation. On the other hand, aiming at the non-stationary channel, a channel estimation and interpolation method based on extended Kalman filter (EKF) and unscented Kalman filter (UKF) Bayesian filters to jointly estimate the channel impulse response (CIR) and time-varying time domain autocorrelation coefficient is adopted. Through the MATLAB simulation, the robustness and reliability of end-to-end communication for V2V and IIoT are promoted by the proposed algorithms.
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