Locally Linear Back-propagation Based Contribution for Nonlinear Process Fault Diagnosis

This paper proposes a novel locally linear backpropagation based contribution(LLBBC) for nonlinear process fault diagnosis. As a method based on the deep learning model of auto-encoder(AE), LLBBC can deal with the fault diagnosis problem through extracting nonlinear features. When the on-line fault diagnosis task is in progress, a locally linear model is firstly built at the current fault sample. According to the basic idea of reconstruction based contribution(RBC), the propagation of fault information is described by using back-propagation(BP) algorithm. Then, a contribution index is established to measure the correlation between the variable and the fault, and the final diagnosis result is obtained by searching variables with large contributions. The smearing effect, which is an important factor affecting the performance of fault diagnosis, can be suppressed as well,and the theoretical analysis reveals that the correct diagnosis can be guaranteed by LLBBC. Finally, the feasibility and effectiveness of the proposed method are verified through a nonlinear numerical example and the Tennessee Eastman benchmark process.

Kernel Generalization of Multi-Rate Probabilistic Principal Component Analysis for Fault Detection in Nonlinear Process

In practical process industries,a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes,which indicates that the measurements coming from different sources are collected at different sampling rates.To build a complete process monitoring strategy,all these multi-rate measurements should be considered for data-based modeling and monitoring.In this paper,a novel kernel multi-rate probabilistic principal component analysis(K-MPPCA)model is proposed to extract the nonlinear correlations among different sampling rates.In the proposed model,the model parameters are calibrated using the kernel trick and the expectation-maximum(EM)algorithm.Also,the corresponding fault detection methods based on the nonlinear features are developed.Finally,a simulated nonlinear case and an actual pre-decarburization unit in the ammonia synthesis process are tested to demonstrate the efficiency of the proposed method.

Data-driven soft sensors in blast furnace ironmaking:a survey

The blast furnace is a highly energy-intensive,highly polluting,and extremely complex reactor in the ironmaking process.Soft sensors are a key technology for predicting molten iron quality indices reflecting blast furnace energy consumption and operation stability,and play an important role in saving energy,reducing emissions,improving product quality,and producing economic benefits.With the advancement of the Internet of Things,big data,and artificial intelligence,data-driven soft sensors in blast furnace ironmaking processes have attracted increasing attention from researchers,but there has been no systematic review of the data-driven soft sensors in the blast furnace ironmaking process.This review covers the state-of-the-art studies of data-driven soft sensors technologies in the blast furnace ironmaking process.Specifically,wefirst conduct a comprehensive overview of various data-driven soft sensor modeling methods(multiscale methods,adaptive methods,deep learning,etc.)used in blast furnace ironmaking.Second,the important applications of data-driven soft sensors in blast furnace ironmaking(silicon content,molten iron temperature,gas utilization rate,etc.)are classified.Finally,the potential challenges and future development trends of data-driven soft sensors in blast furnace ironmaking applications are discussed,including digital twin,multi-source data fusion,and carbon peaking and carbon neutrality.

Object servoing of differential-drive service robots using switched control

Object servoing is becoming more and more important for service robots. Because of the nonholonomicconstraints of a differential-drive service robot and the possibly changed pose of amovable object, it is challenging to design an object servoing scheme for the differential-driveservice robot such that it can asymptotically park at a predefined relative pose to that of the movableobject. In this paper, a novel object servoing scheme is proposed for the differential-driveservice robots using switched control. Each relative online pose is first estimated by using featuresof the movable object, the estimated pose is an input of an object servoing friendly parkingcontroller. The linear velocity and angular speed are then determined by the proposed controller.Simulation results validate the performance of the proposed object servoing scheme. Due to itslow online computational cost, the proposed scheme can be applied for the real-time tasks ofdifferential-drive service robots to movable objects.

Nonlinear fault detection based on locally linear embedding

In this paper, a new nonlinear fault detection technique based on locally linear embedding （LLE） is developed. LLE can efficiently compute the low-dimensional embedding of the data with the local neighborhood structure information preserved. In this method, a data-dependent kernel matrix which can reflect the nonlinear data structure is defined. Based on the kernel matrix, the Nystrrm formula makes the mapping extended to the testing data possible. With the kernel view of the LLE, two monitoring statistics are constructed. Together with the out of sample extensions, LLE is used for nonlinear fault detection. Simulation cases were studied to demonstrate the performance of the proposed method.

Identification of important factors influencing nonlinear counting systems

Identifying factors that exert more influence on system output from data is one of the most challenging tasks in science and engineering.In this work,a sensitivity analysis of the generalized Gaussian process regression(SA-GGPR)model is proposed to identify important factors of the nonlinear counting system.In SA-GGPR,the GGPR model with Poisson likelihood is adopted to describe the nonlinear counting system.The GGPR model with Poisson likelihood inherits the merits of nonparametric kernel learning and Poisson distribution,and can handle complex nonlinear counting systems.Nevertheless,understanding the relationships between model inputs and output in the GGPR model with Poisson likelihood is not readily accessible due to its nonparametric and kernel structure.SA-GGPR addresses this issue by providing a quantitative assessment of how different inputs affect the system output.The application results on a simulated nonlinear counting system and a real steel casting-rolling process have demonstrated that the proposed SA-GGPR method outperforms several state-of-the-art methods in identification accuracy.