Optimum parameters for fault detection and diagnosis system of batch reaction using multiple neural networks

Tan, Wen; Nor, Nursyarizal Mohd; Bakar, M. Zabri Abu; Ahmad, Zainal Arifin; Sata, Suhairi A.

doi:10.1016/j.jlp.2011.08.002

Cited by 28 publications

(9 citation statements)

References 5 publications

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“…According to Nasr et al (2012) ANNs provide an effective analysing and diagnosing tool to understand and simulate the nonlinear behaviour of complex systems. As more data describing the system condition and its influencing parameters become available, data-based methods are being increasingly applied in the field of fault detection (Tan et al 2012), fault diagnostics (Tamilselvan and Wang 2013) and for predicting the residual useful life (Tian et al 2010).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Application of NARX neural network for predicting marine engine performance parameters

Raptodimos

Lazakis

2019

Ships and Offshore Structures

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Though the maritime industry is still predominantly reliant on a time-based, prescriptive approach to maintenance, the increasing complexity of shipboard systems, heightened expectation and competitive requirements as to ship availability and efficiency and the influence of the data revolution on vessel operations, favour a properly structured Condition Based Maintenance (CBM) regime. In this respect Artificial Neural Networks (ANNs) can be applied for predictive maintenance strategies that can assist decision makers in selecting appropriate maintenance actions for critical ship machinery. This paper focuses on developing a Nonlinear Autoregressive with Exogenous Input (NARX) ANN model for forecasting future values of performance parameters of a marine main engine. Moreover, a detailed sensitivity analysis is conducted to examine the performance and robustness of the developed NARX model, based on the dataset applied for its training. Through the NARX model, a predictive monitoring approach can be achieved for ship machinery monitoring as high forecasting accuracy was achieved for the case study of a main engine cylinder exhaust gas outlet temperature. The sensitivity analysis overall demonstrated the good performance and generalisation of the NARX model as it successfully considers different values of the time series data for conducting the onestep-ahead output.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Application of NARX neural network for predicting marine engine performance parameters

Raptodimos

Lazakis

2019

Ships and Offshore Structures

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“…One of the intelligent fault diagnosing techniques is neural network systems. Because of their high potential for capturing nonlinear relationships, neural networks represent a powerful tool for fault diagnosis [43][44][45][46][47]. In fault detection based on neural networks, the number of neurons in the input and output layers are equal to the number of measured variables and the number of potential faults in the process, respectively.…”

Section: Application Of Ai Techniques In Fault Detection and Diagnosis Of Chemical Engineeringmentioning

confidence: 99%

Application of AI in Chemical Engineering

Hajjar¹,

Tayyebi²,

EghbalAhmadi³

2018

Artificial Intelligence - Emerging Trends and Applications

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A major shortcoming of traditional strategies is the fact that solving chemical engineering problems due to the highly nonlinear behavior of chemical processes is often impossible or very difficult. Today, artificial intelligence (AI) techniques are becoming useful due to simple implementation, easy designing, generality, robustness and flexibility. The AI includes various branches, namely, artificial neural network, fuzzy logic, genetic algorithm, expert systems and hybrid systems. They have been widely used in various applications of the chemical engineering field including modeling, process control, classification, fault detection and diagnosis. In this chapter, the capabilities of AI are investigated in various chemical engineering fields.

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“…Fault detection (FD) is essential to observe the continuity of good functioning of the system under typical circumstances for ensuring safety. ()…”

Section: Introductionmentioning

confidence: 99%

Model‐based and data‐driven with multiscale sum of squares double EWMA control chart for fault detection in biological systems

Mansouri

Harkat

Teh

et al. 2018

Journal of Chemometrics

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The objectives of this paper will be sought. First, an enhanced technique that can accurately model biological processes will be developed. To deal with scenarios where a process model is available, the particle filter method will be developed to better handle the nonlinear and high-dimensional state estimation problem.Second, a multiscale sum of squares double exponentially weighted moving average (MS-SS-DEWMA) chart will be applied to the monitored residuals in order to enhance the fault detection abilities. The advantage of MS-SS-DEWMA chart is twofold: (1) The SS-DEWMA chart uses the sum of squares statistics; it simultaneously monitors the process mean and variance in a single chart. It has presented better performance than the classical EWMA-based charts.(2) The multiscale data representation can be used as an effective tool for reducing noise from a signal's time series. The effectiveness of the proposed strategy is validated using a synthetic and simulated Cad system in Escherichia coli (CSEC) data.When the simulated CSEC model is used, the developed approach is applied for monitoring some of the key variables involved in the CSEC model. The proposed strategy is also applied to detect diseases using genomic copy number data through better detection of aberrations in the genetic information of patients, which can help medical doctors make more accurate diagnosis of diseases.

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Optimum parameters for fault detection and diagnosis system of batch reaction using multiple neural networks

Cited by 28 publications

References 5 publications

Application of NARX neural network for predicting marine engine performance parameters

Application of NARX neural network for predicting marine engine performance parameters

Application of AI in Chemical Engineering

Model‐based and data‐driven with multiscale sum of squares double EWMA control chart for fault detection in biological systems

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