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IEEE Transactions on Automation Science and Engineering
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Year: 2024 | Volume: 21 | Issue: 2
IEEE Transactions on Automation Science and Engineering Publication Information
Publication Year: 2024,Page(s):C2 - C2
IEEE Transactions on Automation Science and Engineering Publication Information
Year: 2024 | Volume: 21 | Issue: 2
Guest Editorial Special Issue on Learning From Imperfect Data for Industrial Automation
Ping Zhou;Xuewu Dai;Kyriakos G. Vamvoudakis;Jan Faigl;Hong Wang
Publication Year: 2024,Page(s):1088 - 1091
Data-Driven Modeling and Operation Optimization With Inherent Feature Extraction for Complex Industrial Processes
Publication Year: 2024,Page(s):1092 - 1106
Cited by: Papers (7)
In response to the tenets of Industry 4.0, operation optimization in industrial processes has become a significant research topic. However, the uncertainties prevailing in the process pose challenges to production operations, especially the feedstock properties. In this work, the operation optimization study is performed on a distillation unit (DU), a typical plant in the industrial process. To en...Show More
A Data-Driven Surrogate Modeling for Train Rescheduling in High-Speed Railway Networks Under Wind-Caused Speed Restrictions
Publication Year: 2024,Page(s):1107 - 1121
Cited by: Papers (3)
In High-Speed Railway (HSR) networks with hub stations connecting multiple HSR lines, Train Timetable Rescheduling (TTR) under disruptions (such as speed restrictions caused by high wind) has been a challenging problem, which requires collaborative consideration of the traffic and impacts on all lines. Compared to the first principle model of complex railway networks, data-driven modeling provides...Show More
Training Demand Prediction Models by Decision Error for Two-Stage Lot-Sizing Problems
Publication Year: 2024,Page(s):1122 - 1137
Cited by: Papers (2)
Demand prediction to support appropriate production decisions is being actively studied. Many prediction models are designed to minimize the prediction error, which is measured by determining the difference between the predicted and ground-truth demand. However, these models ignore the effect of the prediction error on downstream production decisions. This prompted our study, which focuses on dema...Show More
Blackout Missing Data Recovery in Industrial Time Series Based on Masked-Former Hierarchical Imputation Framework
Diju Liu;Yalin Wang;Chenliang Liu;Kai Wang;Xiaofeng Yuan;Chunhua Yang
Publication Year: 2024,Page(s):1138 - 1150
Cited by: Papers (14)
In industrial processes, frequent communication failures and information corruption may result in the loss of entire blocks of industrial process data, which is also known as blackout missing data. The imperfect data of industrial time series impede the performance of subsequent modeling and control tasks. However, traditional matrix factorization or supervised learning data imputation methods are...Show More
Interval Type-2 Fuzzy Neural Network Based on Active Semi-Supervised Learning for Non-Stationary Industrial Processes
Publication Year: 2024,Page(s):1151 - 1162
Cited by: Papers (7)
Accurate models ensure the efficient and safe operations of industrial processes. However, modeling of industrial processes with non-stationary characteristic is challenging. In this study, an interval type-2 fuzzy neural network (IT2FNN) based on active semi-supervised learning (ASSL-IT2FNN) is proposed for such industrial processes. First, an IT2FNN with adaptive fuzzy membership function (FMF) ...Show More
Online Sequential Sparse Robust Neural Networks With Random Weights for Imperfect Industrial Streaming Data Modeling
Publication Year: 2024,Page(s):1163 - 1175
Cited by: Papers (2)
Industrial streaming data exhibits the concept drift characteristic due to the time-variant operating conditions, which degrades the performance of models established by traditional offline batch learning. Moreover, the widespread outliers and the correlations between data variables in industrial data streams can have a devastating impact on modeling. Therefore, this paper presents a novel online ...Show More
Intelligent Control Strategy for Sintering Ignition Temperature Based on Working-Condition Recognition
Publication Year: 2024,Page(s):1176 - 1187
Cited by: Papers (1)
Ignition temperature in a sintering process is an important factor for the quality and yield of sinter ore. Due to the diversity of the calorific value and the flow rate in the sintering process, there are many different steady states in the combustion process. Under different steady states, the change interval and fluctuation range of calorific value and flow rate are completely different which s...Show More
Vision-Locomotion Coordination Control for a Powered Lower-Limb Prosthesis Using Fuzzy-Based Dynamic Movement Primitives
Publication Year: 2024,Page(s):1188 - 1200
Cited by: Papers (9)
An amputee cannot directly use the perceptual visual information to control the movements and gait patterns of his worn prosthesis. In order to help an amputee walk and cross over obstacles smoothly, the paper proposes a vision-locomotion coordination control method for a powered lower-limb prosthesis (PLLP), in which a vision system is proposed to detect obstacles, and a complete vision-locomotio...Show More
Event-Triggered Online Learning Fuzzy-Neural Robust Control for Furnace Temperature in Municipal Solid Waste Incineration Process
Publication Year: 2024,Page(s):1201 - 1213
Cited by: Papers (6)
Affected by high temperature and serious pollution, some key parameters in incinerator are difficult to obtain directly, and the detectable data also contain outliers and noise, which makes it extremely difficult to mine the relationship between variables and derive control rules. To solve these problems, an event-triggered online learning fuzzy-neural robust controller is proposed and applied to ...Show More
Robust Policy Iteration of Uncertain Interconnected Systems With Imperfect Data
Publication Year: 2024,Page(s):1214 - 1222
Cited by: Papers (9)
This paper investigates the robust optimal control problem of a class of continuous-time, partially linear, interconnected systems. In addition to the dynamic uncertainties resulted from the interconnected dynamic system, unknown bounded disturbances are taken into account throughout the learning process, wherein the system’s dynamics and the disturbances are assumed unknown. These challenges lead...Show More
Self-Supervised Learning and Multisource Heterogeneous Information Fusion Based Quality Anomaly Detection for Heavy-Plate Shape
Publication Year: 2024,Page(s):1223 - 1234
Cited by: Papers (2)
Plate shape quality anomaly detection suffers from the problems of less labeled and multisource heterogeneous (MSH) data due to the complex production process with multi-process and multi-equipment. To address these problems, this paper proposes a self-supervised learning framework based on MSH contrastive learning (MSH-CL). In the proposed framework, an encoder is designed to extract the MSH info...Show More
Detection of Rotating Stall Inception of Axial Compressors Based on Deep Dilated Causal Convolutional Neural Networks
Publication Year: 2024,Page(s):1235 - 1243
Cited by: Papers (3)
The anomaly detection of outliers and fault diagnosis in the imperfect time-series data is crucial in aero-engine industry. Early detection of the rotating stall is significant for the active stabilization control of the axial compressors, because the rotating stall and surge can cause enormous vibratory stresses in the blades and gives rise to surge to limit the performance of compressors. An aer...Show More
A New Particle Swarm Optimization Algorithm for Outlier Detection: Industrial Data Clustering in Wire Arc Additive Manufacturing
Jingzhong Fang;Zidong Wang;Weibo Liu;Stanislao Lauria;Nianyin Zeng;Camilo Prieto;Fredrik Sikström;Xiaohui Liu
Publication Year: 2024,Page(s):1244 - 1257
Cited by: Papers (19)
In this paper, a novel outlier detection method is proposed for industrial data analysis based on the fuzzy C-means (FCM) algorithm. An adaptive switching randomly perturbed particle swarm optimization algorithm (ASRPPSO) is put forward to optimize the initial cluster centroids of the FCM algorithm. The superiority of the proposed ASRPPSO is demonstrated over five existing PSO algorithms on a seri...Show More
Fault Diagnosis for Wind Turbine Generators Using Normal Behavior Model Based on Multi-Task Learning
Publication Year: 2024,Page(s):1258 - 1270
Cited by: Papers (2)
Wind turbines (WTs) usually work in harsh environments and complex operating conditions, it leads to operating data of WTs with non-stationary, randomness and outlier. These imperfect data will inevitably impact the fault diagnosis of WTs. In this work, a fault diagnosis method for WT generators using normal behavior model (NBM) based on multi-task learning (MTL) is proposed. The imperfect operati...Show More
Event-Triggered Federated Learning for Fault Diagnosis of Offshore Wind Turbines With Decentralized Data
Shixiang Lu;Zhiwei Gao;Ping Zhang;Qifa Xu;Tianming Xie;Aihua Zhang
Publication Year: 2024,Page(s):1271 - 1283
Cited by: Papers (6)
Rapid developments of offshore wind industry offer a strong demand opportunity for offshore wind turbine remote diagnosis. As offshore wind turbines are often located in harsh and communication-constrained environments, the collection and transmission of data is severely restricted, which poses a serious challenge to the conventional centralized diagnostic paradigm that relies on data aggregation....Show More
Label Propagation With Contrastive Anchors for Deep Semi-Supervised Superheat Degree Identification in Aluminum Electrolysis Process
Publication Year: 2024,Page(s):1284 - 1296
Cited by: Papers (7)
Accurate identification of multimodal Superheat Degree (SD) plays a critical decision-making role in Aluminum Electrolysis Process (AEP). Because the labeled SD data are scarce and annotation is expensive in real-world AEP, it is a challenge to develop a well-behaved SD identification model. In this paper, a Contrastive Anchors-based Label Propagation (CALP) algorithm is proposed to construct a De...Show More
Data-Driven Virtual Sensing for Probabilistic Condition Monitoring of Solenoid Valves
Victor Vantilborgh;Tom Lefebvre;Kerem Eryilmaz;Guillaume Crevecoeur
Publication Year: 2024,Page(s):1297 - 1311
Cited by: Papers (2)
There is an emerging industrial demand for predictive maintenance algorithms that exhibit high levels of predictive accuracy. Such condition monitoring tools must estimate dynamic quantities, such as Remaining Useful Lifetime (RUL) and the State of Health (SOH), based on a, typically, restricted set of measurements that can be obtained in an operational setting. These quantities exhibit inherent s...Show More
Prediction-Based Power Consumption Monitoring of Industrial Equipment Using Interpretable Data-Driven Models
Publication Year: 2024,Page(s):1312 - 1322
Cited by: Papers (5)
Efficient energy optimization and scheduling in industrial factories depend on accurate, reasonable, and real-time monitoring of equipment power consumption. However, the power prediction of industrial equipment requires a large number of process data, which could be inevitably contaminated by some imperfect data, due to the harsh environments. Monitoring or predicting equipment power consumption ...Show More
A Review of Robust Machine Scheduling
Publication Year: 2024,Page(s):1323 - 1334
Cited by: Papers (3)
Robust optimization (RO) has been recognized as an effective means to deal with unanticipated events in highly uncertain and risky environments. This paper systematically reviews two types of emerging RO machine scheduling approaches—robust machine scheduling (R-MS) and distributionally R-MS (DR-MS) methods—which usually offer tractable formulations and analytical results for machine scheduling pr...Show More
Finite-Time Adaptive Fuzzy Backstepping Control for Quadrotor UAV With Stochastic Disturbance
Dengxiu Yu;Shizhuo Ma;Yan-Jun Liu;Zhen Wang;C. L. Philip Chen
Publication Year: 2024,Page(s):1335 - 1345
Cited by: Papers (24)
This paper proposes an adaptive fuzzy output feedback control approach for quadrotor unmanned aerial vehicles (QUAV) with stochastic disturbances, besides which we consider the unmeasurable states and unknown nonlinear functions. The QUAV system contains stochastic terms which are not bounded and not differentiable. By combining the It $\hat {\rm \textbf {o}}$ differential equation with finite-ti...Show More
Finite-Time Fault-Tolerant Formation Control for Distributed Multi-Vehicle Networks With Bearing Measurements
Publication Year: 2024,Page(s):1346 - 1357
Cited by: Papers (29)
This paper addresses a bearing-only formation tracking problem in robotic networks by considering exogenous disturbances and actuator faults. In contrast to traditional position-based coordination strategies, the bearing-only coordinated movements of the unmanned vehicles only rely on the neighboring bearing information. This feature can be utilized to reduce the sensing requirements in the hardwa...Show More
Measuring Color Index of Transformer Oil–Enabling Single-Wavelength Spectroscopy With Artificial Neural Network-Fuzzy Logic Model
M. H. Hasnul Hadi;Pin Jern Ker;Hui Jing Lee;Yang Sing Leong;Vimal A. Thiviyanathan;M. A. Hannan;Md. Zaini Jamaludin;M. A. Mahdi
Publication Year: 2024,Page(s):1358 - 1368
Cited by: Papers (3)
Conventionally, the color index of transformer oil is determined by a color comparator based on the American Society for Testing and Materials (ASTM) D 1500 standard. The equipment requires humans to operate, which leads to human error and limited number of samples tested per day. This work proposes the utilization of single-wavelength spectroscopy with 405 nm laser diode using artificial neural n...Show More
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