Robust Monitoring and Fault Isolation of Nonlinear Industrial Processes Using Denoising Autoencoder and Elastic Net

Yu, Wanke; Zhao, Chunhui

doi:10.1109/tcst.2019.2897946

Cited by 154 publications

(36 citation statements)

References 39 publications

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“…Autoencoder (AE) is an unsupervised learning method in machine learning. Its basic structure consists of an input layer, several hidden layers, and an output layer, a widely used deep learning model [22], and is mainly used for data dimensionality reduction and feature extraction. First, AE encodes the input features and then decodes them.…”

Section: B Abstract Feature Extractionmentioning

confidence: 99%

Servo Health Monitoring Based on Feature Learning via Deep Neural Network

Zhou

Zheng

Jin³

et al. 2021

IEEE Access

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Section: B Abstract Feature Extractionmentioning

confidence: 99%

Servo Health Monitoring Based on Feature Learning via Deep Neural Network

Zhou

Zheng

Jin³

et al. 2021

IEEE Access

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“…According to the analysis the correlation between the ship's static information matrix, we extract the static information moulded draught (Draught), deadweight tonnage (DWT), length overall (LOA), beam (BM), and moulded depth (Depth) that we need to use in the representation of the ship domain. For normal data that meet the normal epidemic distribution, the distribution characteristics are extracted by AE training [19]. The abnormal ship static information is sparse in data, that is, most of it is sparse data [20], which is mainly represented by the lack (NULL) or zero of certain element information data.…”

Section: A Static Information Pre-processingmentioning

confidence: 99%

Ship Collision Risk Assessment Based on Collision Detection Algorithm

Liu

Shi

2020

IEEE Access

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Ningbo Zhoushan port handled 1.08 billion tons cargoes in 2018 which is considered as the one of biggest ports in the world. There are more than 1 000 ships enter or depart the port per day. Therefore, it is of importance to assess the collision risk for ships passing through the harbor area. In this paper, a novel approach is initially proposed to assess ship collision risk in the harbor area based on collision detection technology of ship domain using automatic identified system (AIS) data. This study aims to build a unified framework of collision risk assessment which does not need to build different models in accordance with the ship domain we selected. To clean the historical motion data of ships, a method for anomaly detection of ship static information based on autoencoder (AE) is proposed. Based on the above proposed method, the ship collision frequency can be estimated, besides, the risk area can also be determined. The results obtained from the method could provide a reference on furthering enhance the navigational safety for the Maritime and Port Authority.

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“…The original ANFIS model achieves the network parameters by solving a least squares problem using the gradient-based optimization algorithm to fit the nonlinear relations in CIPs. It has demonstrated that the converging speed of an ANFIS is slow and it is easy to fall into a local minimum of the nonlinear system [28]. The FIS's structure makes its rule number and parameter quantity increase with a power-law relationship with the number of input features.…”

Section: Introductionmentioning

confidence: 99%

STA-APSNFIS: STA-Optimized Adaptive Pre-Sparse Neuro-Fuzzy Inference System for Online Soft Sensor Modeling

Liu

Jiang

et al. 2020

IEEE Access

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In complex industrial processes (CIPs), due to technical and economic limitations, key performance indicators (KPIs), especially the chemical content-related KPIs, are often difficult to measure in real time, which hinders the propagation of advanced process control technologies. This paper presents a soft sensor-based online KPI inference scheme by a state transition algorithm (STA)-optimized adaptive pre-sparse neuro-fuzzy inference system model, called STA-APSNFIS. It introduces a pre-sparse neural network to the traditional adaptive neuro-fuzzy inference system (ANFIS) model to establish an adaptive pre-sparse neuro-fuzzy inference system (APSNFIS) model to alleviate the adverse effects of data redundancy and noise interference in the detectable process monitoring data, which can effectively reduce the complexity of neuro-fuzzy inference system (NFIS) and speed up its convergence. Successively, to avoid being trapped at a local optimum, the STA-based optimization algorithm is adopted to replace the traditional gradient-based optimization approach to achieve an optimal APSNFIS model. Extensive validation and comparative experiments on nonlinear numeric simulation systems, benchmark Tenessee Eastman (TE) process and a real industrial bauxite flotation process demonstrated that the proposed STA-APSNFIS performed favorably against traditional ANFIS model as well as its variants, e.g., PSO-ANFIS, GA-ANFIS, and some other soft sensor-based KPI inference models.

show abstract

Robust Monitoring and Fault Isolation of Nonlinear Industrial Processes Using Denoising Autoencoder and Elastic Net

Cited by 154 publications

References 39 publications

Servo Health Monitoring Based on Feature Learning via Deep Neural Network

Servo Health Monitoring Based on Feature Learning via Deep Neural Network

Ship Collision Risk Assessment Based on Collision Detection Algorithm

STA-APSNFIS: STA-Optimized Adaptive Pre-Sparse Neuro-Fuzzy Inference System for Online Soft Sensor Modeling

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