Fault detection and classification for complex processes using semi-supervised learning algorithm

Wang, Xiaogang; Feng, Haichao; Fan, Yunpeng

doi:10.1016/j.chemolab.2015.10.019

Cited by 21 publications

(9 citation statements)

References 26 publications

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“…A review of Machine Learning techniques used in intelligent fault detection was conducted by Lei et al [ 27 ], and their challenges were outlined. It is important to note that there are several studies that use semi-supervised ML methods in fault detection of manufacturing equipment, as seen in [ 28 , 29 , 30 ], but a discussion of these topics is beyond the scope of this paper.…”

Section: Review Of Key Concepts and Trendsmentioning

confidence: 99%

Smart Prognostics and Health Management (SPHM) in Smart Manufacturing: An Interoperable Framework

Sundaram

Zeid

2021

Sensors

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Advances in the manufacturing industry have led to modern approaches such as Industry 4.0, Cyber-Physical Systems, Smart Manufacturing (SM) and Digital Twins. The traditional manufacturing architecture that consisted of hierarchical layers has evolved into a hierarchy-free network in which all the areas of a manufacturing enterprise are interconnected. The field devices on the shop floor generate large amounts of data that can be useful for maintenance planning. Prognostics and Health Management (PHM) approaches use this data and help us in fault detection and Remaining Useful Life (RUL) estimation. Although there is a significant amount of research primarily focused on tool wear prediction and Condition-Based Monitoring (CBM), there is not much importance given to the multiple facets of PHM. This paper conducts a review of PHM approaches, the current research trends and proposes a three-phased interoperable framework to implement Smart Prognostics and Health Management (SPHM). The uniqueness of SPHM lies in its framework, which makes it applicable to any manufacturing operation across the industry. The framework consists of three phases: Phase 1 consists of the shopfloor setup and data acquisition steps, Phase 2 describes steps to prepare and analyze the data and Phase 3 consists of modeling, predictions and deployment. The first two phases of SPHM are addressed in detail and an overview is provided for the third phase, which is a part of ongoing research. As a use-case, the first two phases of the SPHM framework are applied to data from a milling machine operation.

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Section: Review Of Key Concepts and Trendsmentioning

confidence: 99%

Smart Prognostics and Health Management (SPHM) in Smart Manufacturing: An Interoperable Framework

Sundaram

Zeid

2021

Sensors

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“…There are two types of machine learning (ML) methods towards fault detection. a) Supervised learning (SL) approaches (Wang et al 2015), where the fault detection ML model needs to be trained with some expert knowledge. This means that there should be some previous information on how faulty the data is.…”

Section: Machine Learning For Fault Detectionmentioning

confidence: 99%

Towards fault detection and self-healing of chemical processes over wireless sensor networks

Dorneanu¹,

Ruan²,

Mohamed³

et al. 2020

Industry 4.0 – Shaping the Future of the Digital World

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“…As explained above, after the fault detection tasks, fault isolation and diagnosis should be conducted to pinpoint the root causes of occurring faults. If there exist abundant historical faulty samples whose class labels are associated with all possible fault categories, multi-class classifiers [19][20][21][22] can be explicitly constructed. The designed classifiers assign the most similar fault categories to query samples; faults are diagnosed without the fault isolation procedure.…”

Section: Introductionmentioning

confidence: 99%

A Fault Isolation Method via Classification and Regression Tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power Plant

Jang²,

Yoo³

et al. 2018

Energies

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Accurate detection and isolation of possible faults are indispensable for operating complex industrial processes more safely, effectively, and economically. In this paper, we propose a fault isolation method for steam boilers in thermal power plants via classification and regression tree (CART)-based variable ranking. In the proposed method, binary classification trees are constructed by applying the CART algorithm to a training dataset which is composed of normal and faulty samples for classifier learning then, to perform faulty variable isolation, variable importance values for each input variable are extracted from the constructed trees. The importance values for non-faulty variables are not influenced by faulty variables, because the values are extracted from the trees with decision boundaries only in the original input space; the proposed method does not suffer from smearing effect. Furthermore, the proposed method, based on the nonparametric CART classifier, can be applicable to nonlinear processes. To confirm the effectiveness, the proposed and comparison methods are applied to two benchmark problems and 250 MW drum-type steam boiler. Experimental results show that the proposed method isolates faulty variables more clearly without the smearing effect than the comparison methods.

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Fault detection and classification for complex processes using semi-supervised learning algorithm

Cited by 21 publications

References 26 publications

Smart Prognostics and Health Management (SPHM) in Smart Manufacturing: An Interoperable Framework

Smart Prognostics and Health Management (SPHM) in Smart Manufacturing: An Interoperable Framework

Towards fault detection and self-healing of chemical processes over wireless sensor networks

A Fault Isolation Method via Classification and Regression Tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power Plant

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