Impeller fault detection under variable flow conditions based on three feature extraction methods and artificial neural networks

Jami, Amin; Heyns, P.S.

doi:10.1007/s12206-018-0807-3

Cited by 13 publications

(7 citation statements)

References 18 publications

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“…There are various studies from different areas in the literature that are based on machine learning techniques for anomaly detection, e.g., [2][3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Method Based on an Autoencoder for Anomaly Detection in DC Motor Body Temperature

Demircioğlu

Yilmaz

2023

Applied Sciences

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Anomaly detection has an important role in industrial systems. Abnormal situations occurring in a system cause anomalies, and the anomalies reduce system performance over time, and may also make the system malfunction. Therefore, the correct and timely detection of anomalies is of critical importance for predictive maintenance. In this study, an autoencoder-based method is proposed for anomaly detection in DC motor body temperature. The performance of the method was examined on a dataset that was created specifically for this study. In the experiments, the three-sigma outlier method was also applied on the same dataset for the same purpose and its performance results are used for comparison. The performance results of both methods are represented in terms of three measures, namely, accuracy, recall, and precision. The experimental study showed that the proposed method achieved over 96% ratios for all three measures, and it can be successfully used for anomaly detection in DC motor body temperature. Additionally, it can be concluded that the proposed system can be preferred for anomaly detection in time series data collected from different types of sensors when the performance results are taken into consideration.

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“…There are various studies from different areas in the literature that are based on machine learning techniques for anomaly detection, e.g., [2][3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Method Based on an Autoencoder for Anomaly Detection in DC Motor Body Temperature

Demircioğlu

Yilmaz

2023

Applied Sciences

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“…The traditional manual analysis method is difficult to popularize because it needs considerable manpower and material resources and is affected by professional experience. Due to the good nonlinear approximation ability of artificial neural networks, back propagation neural networks [17], radial basis function neural networks [18], wavelet neural networks [19], extreme learning machines [20], self-organizing neural networks [21,22] and other models have been applied to the working condition diagnosis of sucker-rod pump wells and are gradually replacing traditional manual analysis methods. However, limited by the mechanism of the model, these methods have the following problems: (1) The input of the model is hundreds of load and displacement data measurements, which makes the internal mapping structure of the model complex and seriously affects the diagnostic accuracy of the model [23]; (2) The working condition diagnosis is based on the shape feature of a dynamometer card, and the input of load and displacement data makes the model unable to extract the shape feature of the dynamometer card directly and effectively.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Diagnosis Method of the Working Conditions in Sucker-Rod Pump Wells Based on Convolutional Neural Networks and Transfer Learning

Zhang¹,

Wang²,

Chen³

2021

Energy Engineering

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In recent years, deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields. In the diagnosis of sucker-rod pump working conditions, due to the lack of a large-scale dynamometer card data set, the advantages of a deep convolutional neural network are not well reflected, and its application is limited. Therefore, this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning, which is used to solve the problem of too few samples in a dynamometer card data set. Based on the dynamometer cards measured in oilfields, image classification and preprocessing are conducted, and a dynamometer card data set including 10 typical working conditions is created. On this basis, using a trained deep convolutional neural network learning model, model training and parameter optimization are conducted, and the learned deep dynamometer card features are transferred and applied so as to realize the intelligent diagnosis of dynamometer cards. The experimental results show that transfer learning is feasible, and the performance of the deep convolutional neural network is better than that of the shallow convolutional neural network and general fully connected neural network. The deep convolutional neural network can effectively and accurately diagnose the working conditions of sucker-rod pump wells and provide an effective method to solve the problem of few samples in dynamometer card data sets.

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“…e existing digital resources are helpful to automatically identify the shape and characteristics of indicator diagrams, which have become a hot spot in the research of oil development [9,10]. Currently, some methods, e.g., the back-propagation (BP) neural networks [11,12], the radial basis function (RBF) [13], the extreme learning machine [14], and the convolution neural networks (CNNs) [15], have been applied to the fault diagnosis of pumping units and are gradually replacing the traditional artificial analysis.…”

Section: Introductionmentioning

confidence: 99%

Fault Recognition of Indicator Diagrams Based on the Dynamic Time Warping Distance of Differential Curves

Zhao

Zhang

2021

Mathematical Problems in Engineering

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Oil recovery with pumping units is widely used in oilfield development at present. The management level of pumping units is related to the overall economic benefit of oilfield. In order to effectively control the production management of pumping wells, it is necessary to take all the production data to formulate a reasonable working system and make continuous analyses about the daily management level of pumping wells. The analysis and interpretation of the indicator diagram is a direct understanding way about the situation of deep well pumps in petroleum engineering. However, the complicated practical oilfield conditions often lead to the distortion of regular indicator diagrams, resulting in the low efficiency of fault-type diagnosis and wrong recognition. A new method of identifying the fault type of indicator diagrams is proposed by using the dynamic time warping (DTW) distance, through which a two-dimensional indicator diagram is transformed into a one-dimensional differential curve. The experimental results of real indicator diagrams show that the method is effective for the type diagnosis of indicator diagrams with high accuracy and fast speed.

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Impeller fault detection under variable flow conditions based on three feature extraction methods and artificial neural networks

Cited by 13 publications

References 18 publications

A Method Based on an Autoencoder for Anomaly Detection in DC Motor Body Temperature

A Method Based on an Autoencoder for Anomaly Detection in DC Motor Body Temperature

An Intelligent Diagnosis Method of the Working Conditions in Sucker-Rod Pump Wells Based on Convolutional Neural Networks and Transfer Learning

Fault Recognition of Indicator Diagrams Based on the Dynamic Time Warping Distance of Differential Curves

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