Variational Inference Based Kernel Dynamic Bayesian Networks for Construction of Prediction Intervals for Industrial Time Series With Incomplete Input

Prediction intervals(PIs)for industrial time series can provide useful guidance for workers.Given that the failure of industrial sensors may cause the missing point in inputs,the existing kernel dynamic Bayesian networks(KDBN),serving as an effective method for PIs construction,suffer from high computational load using the stochastic algorithm for inference.This study proposes a variational inference method for the KDBN for the purpose of fast inference,which avoids the timeconsuming stochastic sampling.The proposed algorithm contains two stages.The first stage involves the inference of the missing inputs by using a local linearization based variational inference,and based on the computed posterior distributions over the missing inputs the second stage sees a Gaussian approximation for probability over the nodes in future time slices.To verify the effectiveness of the proposed method,a synthetic dataset and a practical dataset of generation flow of blast furnace gas(BFG)are employed with different ratios of missing inputs.The experimental results indicate that the proposed method can provide reliable PIs for the generation flow of BFG and it exhibits shorter computing time than the stochastic based one.

Adaptive Attitude Control for a Coaxial Tilt-Rotor UAV via Immersion and Invariance Methodology

Dear Editor,This letter is concerned with the attitude control for a novel tiltrotor unmanned aerial vehicle with two pairs of tiltable coaxial rotors and one rear rotor.An immersion and invariance-based adaptive attitude controller for the tilt-rotor unmanned aerial vehicle is proposed.In the proposed control strategy,an adaptive update law is specially designed to compensate for the uncertainties of damping coefficients.The stability of the resulting closed-loop coaxial tiltrotor unmanned aerial vehicle(CTRUAV)system is proved by the Lyapunov methodology and LaSalle’s invariance theory.Finally。

Data-Driven Based Fault Prognosis for Industrial Systems:A Concise Overview

Fault prognosis is mainly referred to the estimation of the operating time before a failure occurs,which is vital for ensuring the stability,safety and long lifetime of degrading industrial systems.According to the results of fault prognosis,the maintenance strategy for underlying industrial systems can realize the conversion from passive maintenance to active maintenance.With the increased complexity and the improved automation level of industrial systems,fault prognosis techniques have become more and more indispensable.Particularly,the datadriven based prognosis approaches,which tend to find the hidden fault factors and determine the specific fault occurrence time of the system by analysing historical or real-time measurement data,gain great attention from different industrial sectors.In this context,the major task of this paper is to present a systematic overview of data-driven fault prognosis for industrial systems.Firstly,the characteristics of different prognosis methods are revealed with the data-based ones being highlighted.Moreover,based on the different data characteristics that exist in industrial systems,the corresponding fault prognosis methodologies are illustrated,with emphasis on analyses and comparisons of different prognosis methods.Finally,we reveal the current research trends and look forward to the future challenges in this field.This review is expected to serve as a tutorial and source of references for fault prognosis researchers.

Kinetic parameter estimation for cooling crystallization process based on cell average technique and automatic differentiation

In this paper,a cell average technique(CAT)based parameter estimation method is proposed for cooling crystallization involved with particle growth,aggregation and breakage,by establishing a more efficient and accurate solution in terms of the automatic differentiation(AD)algorithm.To overcome the deficiency of CAT that demands high computation cost for implementation,a set of ordinary differential equations(ODEs)entailed from CAT based discretized population balance equation(PBE)are solved by using the AD based high-order Taylor expansion.Moreover,an AD based trust-region reflective(TRR)algorithm and another interior-point(IP)algorithm are established for estimating the kinetic parameters associated with particle growth,aggregation and breakage.As a result,the estimation accuracy can be further improved while the computation cost can be significantly reduced,compared to the existing algorithms.Benchmark examples from the literature are used to illustrate the accuracy and efficiency of the AD-based CAT,TRR and IP algorithms in comparison with the existing algorithms.Moreover,seeded batch cooling crystallization experiments ofβform L-glutamic acid are performed to validate the proposed method.

Formal management-specifying approach for model-based safety assessment

In the field of model-based system assessment,mathematical models are used to interpret the system behaviors.However,the industrial systems in this intelligent era will be more manageable.Various management operations will be dynamically set,and the system will be no longer static as it is initially designed.Thus,the static model generated by the traditional model-based safety assessment(MBSA)approach cannot be used to accurately assess the dependability.There mainly exists three problems.Complex:huge and complex behaviors make the modeling to be trivial manual;Dynamic:though there are thousands of states and transitions,the previous model must be resubmitted to assess whenever new management arrives;Unreusable:as for different systems,the model must be resubmitted by reconsidering both the management and the system itself at the same time though the management is the same.Motivated by solving the above problems,this research studies a formal management specifying approach with the advantages of agility modeling,dynamic modeling,and specification design that can be re-suable.Finally,three typical managements are specified in a series-parallel system as a demonstration to show the potential.

Aeroengine thrust estimation and embedded verification based on improved temporal convolutional network

Thrust estimation is a significant part of aeroengine thrust control systems.The traditional estimation methods are either low in accuracy or large in computation.To further improve the estimation effect,a thrust estimator based on Multi-layer Residual Temporal Convolutional Network(M-RTCN)is proposed.To solve the problem of dead Rectified Linear Unit(ReLU),the proposed method uses the Gaussian Error Linear Unit(GELU)activation function instead of ReLU in residual block.Then the overall architecture of the multi-layer convolutional network is adjusted by using residual connections,so that the network thrust estimation effect and memory consumption are further improved.Moreover,the comparison with seven other methods shows that the proposed method has the advantages of higher estimation accuracy and faster convergence speed.Furthermore,six neural network models are deployed in the embedded controller of the micro-turbojet engine.The Hardware-in-the-Loop(HIL)testing results demonstrate the superiority of M-RTCN in terms of estimation accuracy,memory occupation and running time.Finally,an ignition verification is conducted to confirm the expected thrust estimation and real-time performance.

A Comprehensive Survey on STP Approach to Finite Games

Nowadays the semi-tensor product(STP)approach to finite games has become a promising new direction.This paper provides a comprehensive survey on this prosperous field.After a brief introduction for STP and finite(networked)games,a description for the principle and fundamental technique of STP approach to finite games is presented.Then several problems and recent results about theory and applications of finite games via STP are presented.A brief comment about the potential use of STP to artificial intelligence is also proposed.