Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data

Zhang, Yuequn; Luo, Lei; Ji, Xu; Dai, Yiyang

doi:10.3390/s21206715

Cited by 12 publications

(6 citation statements)

References 69 publications

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“…Zhang et al [105] used fuzzy neural network to establish the normal behavior model of key equipment parameters, calculated the residual between the sample set data and the normal model, constructed a multivariate Gaussian distribution model of the residual, and set the abnormal state threshold by using the contour of Gaussian probability density. In addition, methods such as SVM [106] , least squares SVM [107] , random forest [108] , and gradient boosting [109] are also often applied to anomaly detection.…”

Section: Data-driven Methodsmentioning

confidence: 99%

Artificial Intelligence Methods Applied to Catalytic Cracking Processes

Yang

Lei

et al. 2023

Big Data Min. Anal.

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Fluidic Catalytic Cracking (FCC) is a complex petrochemical process affected by many highly non-linear and interrelated factors. Product yield analysis, flue gas desulfurization prediction, and abnormal condition warning are several key research directions in FCC. This paper will sort out the relevant research results of the existing Artificial Intelligence (AI) algorithms applied to the analysis and optimization of catalytic cracking processes, with a view to providing help for the follow-up research. Compared with the traditional mathematical mechanism method, the AI method can effectively solve the difficulties in FCC process modeling, such as high-dimensional, nonlinear, strong correlation, and large delay. AI methods applied in product yield analysis build models based on massive data. By fitting the functional relationship between operating variables and products, the excessive simplification of mechanism model can be avoided, resulting in high model accuracy. AI methods applied in flue gas desulfurization can be usually divided into two stages: modeling and optimization. In the modeling stage, data-driven methods are often used to build the system model or rule base; In the optimization stage, heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule base. AI methods, including data-driven and knowledge-driven algorithms, are widely used in the abnormal condition warning. Knowledge-driven methods have advantages in interpretability and generalization, but disadvantages in construction difficulty and prediction recall. While the data-driven methods are just the opposite. Thus, some studies combine these two methods to obtain better results.

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Section: Data-driven Methodsmentioning

confidence: 99%

Artificial Intelligence Methods Applied to Catalytic Cracking Processes

Yang

Lei

et al. 2023

Big Data Min. Anal.

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“…Likewise, another mathematical model based on N-manifolds may also be considered [51]. On the other hand, such procedures may not be really accurate when some parameters are missing, hence random forests based on decision paths are also proposed, leading to the proposition of an improved random forest based on decision paths [52]. With respect to decision trees, the use of multi-sensor data fusion with different fusion layers offers interesting solutions.…”

Section: Fault Diagnosis and Detectionmentioning

confidence: 99%

Study on Data Center Network Topologies for Monitoring Data using Edge/Fog Computing

ROİG,

ALCARAZ,

GILLY

et al. 2023

Journal of Polytechnic

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The election of an appropriate data center network topology is key when dealing with surveillance and monitoring processes, such as those devoted to obtaining relevant data for sensor data fusion in any type of remote computing environment so as to perform fault diagnosis and prevention. In this paper, some of the most commonly used topologies to interconnect nodes within a data center bound to edge/fog computing, representing either switch-centric ones or server-centric ones, are reviewed and analyzed from a statistical point of view in order to measure their performance, resulting in server-centric ones doing it better.

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“…The random forest (RF) classifier is an ensemble learning algorithm that uses a combination of decision trees and implements voting laws for achieving statistical learning. 43 The RF classifier can deal with the nonlinear data, and it has found various applications, such as sample identification, feature selection, and classification of various machineries. [44][45][46] The above-prepared datasets (dataset I, II, and III) are supplied as input to the RF algorithm to detect the defects and discriminate among the gearbox's health states.…”

Section: Random Forest Algorithmmentioning

confidence: 99%

An integrated condition monitoring scheme for health state identification of a multi-stage gearbox through Hurst exponent estimates

Inturi

Balaji

Gyanam

et al. 2022

Structural Health Monitoring

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The vibration and acoustic signals collected from rotating machinery are often non-stationary and aperiodic, and it is a challenge to post-process and extract the defect sensitive health indicators. In this paper, we demonstrate how the estimated Hurst exponent of such measured data can be advantageous for analyzing non-stationary and aperiodic data due to its self-similarity and scale-invariance properties. To illustrate this, the paper demonstrates detection of fault diagnostics of a multi-stage gearbox operating under fluctuating speeds through estimated Hurst exponent of the raw vibration and acoustic signals as a health indicator. Thirteen health states of the gearbox are considered, and the raw vibration and acoustic signals are collected. The Hurst exponents are calculated using three different approaches: generalized Hurst exponent (q = 1, 2, 3, and 4), rescale range statistical (R/S) analysis, and dispersion analysis from the vibration and acoustic signals. Three different health indicator datasets are formulated and subjected to feature learning through conventional machine-learning (decision tree and support vector machine) and advanced machine-learning (deep-learning) classifiers. The effectiveness of these datasets while discriminating between the health states of the gearbox is investigated, yielding classification accuracies of 96.4% when compared with the individual health indicator datasets. The ability of the fault diagnosis and defect severity analysis with reduced reliance on the signal post-processing algorithms is demonstrated.

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Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data

Cited by 12 publications

References 69 publications

Artificial Intelligence Methods Applied to Catalytic Cracking Processes

Artificial Intelligence Methods Applied to Catalytic Cracking Processes

Study on Data Center Network Topologies for Monitoring Data using Edge/Fog Computing

An integrated condition monitoring scheme for health state identification of a multi-stage gearbox through Hurst exponent estimates

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