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With modern industrial processes becoming more and more complex, the occurrence of faults may cause unmitigated disaster. Therefore, incipient fault detection is very important and has attracted increasing attention Recently, a feature ensemble ...

• A dense ensemble network that reuses shallow information is proposed.
• Several unsupervised base detectors with machine learning are constructed.
• The computational complexity of this method is analyzed.
• The effectiveness of the ...

Oil–gas–water three-phase flow has multiple flow states, exhibiting dynamic, nonlinear, and instantaneous behaviors. Monitoring and analysis of flow state are crucial for ensuring safe operation of industrial processes. However, the absence of a ...

• Oil–gas–water three-phase flow state is described by global–local attributes.
• Attribute heat map and a metric corporately offer a comprehensive analysis.
• Ag-MRDCVA extracts features encoding attributes under deep learning ...

Sensor fault detection is an important phase for process surveillance. Indeed, successful execution of process tasks depends on the state of the available data. In industrial applications, systems have an uncertain behavior, so methods based on ...

Kernel methods and neural networks (NNs) are two mainstream nonlinear data modeling methods and have been widely applied to industrial process monitoring. However, they both present imperfect properties, so the relevant applications are limited. ...

Multivariate statistical process monitoring is widely used in industrial processes for performance monitoring and fault detection. Once a fault has been detected, fault diagnoses must be performed to identify the variables that are responsible ...

• A new data-driven method is proposed for variable contribution analysis.
• Method is based on permutation entropy, which detects abnormalities in a system.
• Variables that contribute to or is affected by faults in a process are ...

Quality-related process monitoring is essential for revealing changes in product quality and ensuring industrial safety. Therefore, it is crucial to distinguish enough quality-related features within the data. To learn the nonlinear ...

• A novel semi-supervised quality-related process monitoring method is proposed.
• A novel latent variable selection strategy is designed.
• A novel variable contribution plot method is proposed to identify the responsible fault ...

The stable control of product quality when abnormal working conditions occur in the industrial production process is essential to improve product quality and economic efficiency. However, the process industry suffers from multivariate, nonlinear, ...

The Laser Metal Deposition (LMD) is emerging among the additive manufacturing (AM) technologies of metals for its versatility. It takes advantage of the flexibility of a laser source and a powder flow to fabricate or repair components with very ...

Process monitoring models play an increasingly indispensable role in promptly differentiating faults within the wastewater treatment process to maintain a safe state. The accuracy and time overhead are crucial indicators in judging whether models ...

• Time-SBLS is applied for the first time to the wastewater treatment process.
• Time-SBLS model can achieve satisfactory monitoring accuracy .
• Time-SBLS overcomes the limitations of some deep learning networks.
• Time-SBLS is ...

Artifact-driven process monitoring is an effective technique to autonomously monitor business processes. Instead of requiring human operators to notify when an activity is executed, artifact-driven process monitoring infers this information from ...

Process data collected from real-world industrial operating environments have different distributions and lack real-time labeled samples, which causes the performance of process monitoring to decline. In this paper, a multiple structured latent ...

With the rapid development of additive manufacturing (AM) technology, quality inspection has become one of the most crucial research topics in additive manufacturing. Although numerous image-based deep learning methods have been successfully ...

The randomness and low frequency of laser powder bed fusion defects are two important characteristics that can impact the quality and reliability of parts. Therefore, effectively detecting the forming quality of parts during the manufacturing ...

• A coaxial process monitoring system is utilized to monitor the entire manufacturing process of LPBF.
• A manifold learning-based method shows global optimality is unattainable with unknown features.
• A 2D Transformer-based model ...

Deep neural networks (DNNs) have shown advantages in dealing with complex nonlinear problems and have been applied in process monitoring. However, traditional DNN based process monitoring methods face the following problems. Firstly, only ...

Process monitoring of industrial production has always been one of the main concerns of process industry systems. As artificial intelligence booms, fault detection and diagnosis via deep learning has been widely used in industrial process ...

• Tool breakage monitoring based on the fusion of dimensionless indicators.
• ...

Tool breakage occurs randomly during machining operations, which induces more severe impacts on the quality of components compared to progressive tool wear. It is widely acknowledged that the unpredictable changes in cutting conditions ...

With the predicted depletion of natural resources and alarming environmental issues, sustainable development has become a popular as well as a much-needed concept in modern process industries. Hence, manufacturers are quite keen on adopting novel ...

• This paper provides a detailed description on state-of-the-art of soft sensors.
• This work discusses how the industry can be sustainable via advanced monitoring.
• This provides a good overview on soft sensing of different industries.

Control charts are widely used as a tool in process quality monitoring to detect anomalies and to improve the quality of a process and product. Nevertheless, their limitations have increased in the face of increasingly complex manufacturing ...

Resistance spot welding (RSW) is an important manufacturing process across major industries due to its high production speed and ease of automation. Though conceptually straightforward, the process combines complex electrical, thermal, fluidic, ...

Event logs recorded during the execution of business processes provide a valuable starting point for operational monitoring, analysis, and improvement. Specifically, measures that quantify any deviation between the recorded operations ...

• A framework for privatizing PPIs, and differentially private release mechanisms.