Research

19 pages, 2957 KiB

Open AccessArticle

FMECA and MFCC-Based Early Wear Detection in Gear Pumps in Cost-Aware Monitoring Systems

by Geon-Hui Lee, Ugochukwu Ejike Akpudo and Jang-Wook Hur

Electronics 2021, 10(23), 2939; https://doi.org/10.3390/electronics10232939 - 26 Nov 2021

Cited by 6 | Viewed by 2388

Gear pump failures in industrial settings are common due to their exposure to uneven high-pressure outputs within short time periods of machine operation and uncertainty. Improving the field and line clam are considered as the solutions for these failures, yet they are quite [...] Read more.

Gear pump failures in industrial settings are common due to their exposure to uneven high-pressure outputs within short time periods of machine operation and uncertainty. Improving the field and line clam are considered as the solutions for these failures, yet they are quite insufficient for optimal reliability. This research, therefore, suggests a method for early wear detection in gear pumps following an extensive failure modes, effects, and criticality analysis (FMECA) of an AP3.5/100 external gear pump manufactured by BESCO. To replicate this condition, fine particles of iron oxide (Fe

_{2}

O

_{3}

) were mixed with the experimental fluid, and the resulting vibration data were collected, processed, and exploited for wear detection. The intelligent wear detection process was explored using various machine learning algorithms following a mel-frequency cepstral coefficient (MFCC)-based discriminative feature extraction process. Among these algorithms, extensive performance evaluation reveals that the random forest classifier returned the highest test accuracy of 95.17%, while the k-nearest neighbour was the most cost efficient following cross validations. This study is expected to contribute to improved evaluations of gear pump failure diagnosis and prognostics. Full article

(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control and Their Applications to Aerospace and Mechanical Systems)

► Show Figures

Figure 1

21 pages, 4750 KiB

Open AccessFeature PaperArticle

An Explainable DL-Based Condition Monitoring Framework for Water-Emulsified Diesel CR Systems

by Ugochukwu Ejike Akpudo and Jang-Wook Hur

Electronics 2021, 10(20), 2522; https://doi.org/10.3390/electronics10202522 - 15 Oct 2021

Cited by 1 | Viewed by 1781

Abstract

Despite global patronage, diesel engines still contribute significantly to urban air pollution, and with the ongoing campaign for green automobiles, there is an increasing demand for controlling/monitoring the pollution severity of diesel engines especially in heavy-duty industries. Emulsified diesel fuels provide a readily [...] Read more.

Despite global patronage, diesel engines still contribute significantly to urban air pollution, and with the ongoing campaign for green automobiles, there is an increasing demand for controlling/monitoring the pollution severity of diesel engines especially in heavy-duty industries. Emulsified diesel fuels provide a readily available solution to engine pollution; however, the inherent reduction in engine power, component corrosion, and/or damage poses a major concern for global adoption. Notwithstanding, on-going investigations suggest the need for reliable condition monitoring frameworks to accurately monitor/control the water-diesel emulsion compositions for inevitable cases. This study proposes the use of common rail (CR) pressure differentials and a deep one-dimensional convolutional neural network (1D-CNN) with the local interpretable model-agnostic explanations (LIME) for empirical diagnostic evaluations (and validations) using a KIA Sorento 2004 four-cylinder line engine as a case study. CR pressure signals were digitally extracted at various water-in-diesel emulsion compositions at various engine RPMs, pre-processed, and used for necessary transient and spectral analysis, and empirical validations. Results reveal high model trustworthiness with an average validation accuracy of 95.9%. Full article

(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control and Their Applications to Aerospace and Mechanical Systems)

► Show Figures

Figure 1

17 pages, 3295 KiB

Open AccessArticle

An Integrated Cost-Aware Dual Monitoring Framework for SMPS Switching Device Diagnosis

by Akeem Bayo Kareem, Ugochukwu Ejike Akpudo and Jang-Wook Hur

Electronics 2021, 10(20), 2487; https://doi.org/10.3390/electronics10202487 - 13 Oct 2021

Cited by 7 | Viewed by 1857

Abstract

The ability of a switch-mode AC/DC power supply to shrink supplies is a benefit and a requirement for most electronic devices with limited space. Major failures in switch-mode power supply (SMPS) during adverse working conditions are subject to mostly the switching devices and [...] Read more.

The ability of a switch-mode AC/DC power supply to shrink supplies is a benefit and a requirement for most electronic devices with limited space. Major failures in switch-mode power supply (SMPS) during adverse working conditions are subject to mostly the switching devices and capacitors. For effective condition monitoring of the SMPS, dual (or multiple) sensing provides a more reliable standpoint against the traditional single sensing techniques as it provides a more comprehensive paradigm for accurate condition monitoring. This study proposes an integrated approach to SMPS condition monitoring by exploiting statistically extracted features from current and voltage signals for system fault diagnosis based on electrical stress. Following a correlation-based feature selection approach, salient features were utilized for improved fault detection and isolation (FDI) using ML-based classifiers. Diagnostic results by the classifiers reveal that the random forest and gradient boosting classifiers are highly reliable but computationally expensive when compared with the others while the decision tree was quite cost-efficient with reliable diagnostic results. The proposed framework is effectively applicable for use in diagnosing the switching devices and classification at different states. Full article

(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control and Their Applications to Aerospace and Mechanical Systems)

► Show Figures

Figure 1

17 pages, 4202 KiB

Open AccessArticle

A Cost-Aware DNN-Based FDI Technology for Solenoid Pumps

by Suju Kim, Ugochukwu Ejike Akpudo and Jang-Wook Hur

Electronics 2021, 10(19), 2323; https://doi.org/10.3390/electronics10192323 - 22 Sep 2021

Cited by 11 | Viewed by 1438

Abstract

Fluid Pumps serve a critical function in hydraulic and thermodynamic systems, and this often exposes them to prolonged use, leading to fatigue, stress, contamination, filter clogging, etc. On one hand, vibration monitoring for hydraulic components has shown reliable efficiencies in fault detection and [...] Read more.

Fluid Pumps serve a critical function in hydraulic and thermodynamic systems, and this often exposes them to prolonged use, leading to fatigue, stress, contamination, filter clogging, etc. On one hand, vibration monitoring for hydraulic components has shown reliable efficiencies in fault detection and isolation (FDI) practices. On the other hand, signal processing techniques provide reliable FDI parameters for artificial intelligence (AI)-based data-driven diagnostics (and prognostics) and have recently attracted global interest across different disciplines and applications. Particularly for cost-aware systems, the choice of diagnostic parameters determines the reliability of an FDI/diagnostic model. By extracting (and selecting) discriminative spectral and transient features from solenoid pump vibration signals, accurate diagnostics across operating conditions can be achieved using AI-based FDI algorithms. This study employs a deep neural network (DNN) for fault diagnosis after a correlation-based selection of discriminative spectral and transient features. To solve the problem of hyperparameter selection for the proposed model, a grid search technique was employed for optimal search for parameters (number of layers, neurons, activation function, weight optimizer, etc.) on different network architectures.The results reveal the high accuracy of a three-layer DNN with ReLU activation function, with a test accuracy of 99.23% and a minimal false alarm rate on a case study. Full article

(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control and Their Applications to Aerospace and Mechanical Systems)

► Show Figures

Figure 1

15 pages, 8413 KiB

Open AccessArticle

A Study on Water-Induced Damage Severity on Diesel Engine Injection System Using Emulsified Diesel Fuels

by Min-Seop Kim, Ugochukwu Ejike Akpudo and Jang-Wook Hur

Electronics 2021, 10(18), 2285; https://doi.org/10.3390/electronics10182285 - 17 Sep 2021

Cited by 7 | Viewed by 3296

Abstract

Diesel engine emissions contribute nearly 30% of greenhouse effects and diverse health and environmental problems. Amidst these problems, it is estimated that there will be a 75% increase in energy demand for transportation by 2040, of which diesel fuel constitutes a major source [...] Read more.

Diesel engine emissions contribute nearly 30% of greenhouse effects and diverse health and environmental problems. Amidst these problems, it is estimated that there will be a 75% increase in energy demand for transportation by 2040, of which diesel fuel constitutes a major source of energy for transportation. Being a major source of air pollution, efforts are currently being made to curb the pollution spread. The use of water-in-diesel (W/D)-emulsified fuels comes as a readily available (and cost-effective) option with other benefits including engine thermal efficiency, reduced costs, and NOx reduction; nonetheless, the inherent effects—power loss, component wear, corrosion, etc. still pose strong concerns. This study investigates the behavior and damage severity of a common rail (CR) diesel fuel injection system using exploratory and statistical methods under different W/D emulsion conditions and engine speeds. Results reveal that the effect of W/D emulsion fuels on engine operating conditions are reflected in the CR, which provides a reliable avenue for condition monitoring. Also, the effect of W/D emulsion on injection system components-piston, nozzle needle, and ball seat–are presented alongside related discussions. Full article

(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control and Their Applications to Aerospace and Mechanical Systems)

► Show Figures

Graphical abstract

15 pages, 17475 KiB

Open AccessArticle

Fault-Tolerant Tracking Control for a Descriptor System under an Unknown Input Disturbances

by Norbert Kukurowski, Marcin Pazera and Marcin Witczak

Electronics 2021, 10(18), 2247; https://doi.org/10.3390/electronics10182247 - 13 Sep 2021

Cited by 3 | Viewed by 1315

Abstract

The paper proposes a fault-tolerant tracking control scheme based on a robust observer for a descriptor system. Thus, it is assumed that the described system can be simultaneously occupied by an unknown input disturbance, along with an actuator and sensor faults. Additionally, it [...] Read more.

The paper proposes a fault-tolerant tracking control scheme based on a robust observer for a descriptor system. Thus, it is assumed that the described system can be simultaneously occupied by an unknown input disturbance, along with an actuator and sensor faults. Additionally, it is natural to assume that the unknown input disturbance cannot be estimated, which makes the control process more difficult. Moreover, the proposed descriptor system is also occupied by external disturbances. Thus, the robust stability of the proposed control and estimation scheme was guaranteed by using

H_{\infty}

performance. Consequently, the DC servo-motor laboratory system was used to confirm the correctness and effectiveness of the proposed fault-tolerant tracking control scheme. Full article

(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control and Their Applications to Aerospace and Mechanical Systems)

► Show Figures

Figure 1

18 pages, 3914 KiB

Open AccessEditor’s ChoiceArticle

A CEEMDAN-Assisted Deep Learning Model for the RUL Estimation of Solenoid Pumps

by Ugochukwu Ejike Akpudo and Jang-Wook Hur

Electronics 2021, 10(17), 2054; https://doi.org/10.3390/electronics10172054 - 25 Aug 2021

Cited by 9 | Viewed by 2187

Abstract

This paper develops a data-driven remaining useful life prediction model for solenoid pumps. The model extracts high-level features using stacked autoencoders from decomposed pressure signals (using complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm). These high-level features are then received by [...] Read more.

This paper develops a data-driven remaining useful life prediction model for solenoid pumps. The model extracts high-level features using stacked autoencoders from decomposed pressure signals (using complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm). These high-level features are then received by a recurrent neural network-gated recurrent units (GRUs) for the RUL estimation. The case study presented demonstrates the robustness of the proposed RUL estimation model with extensive empirical validations. Results support the validity of using the CEEMDAN for non-stationary signal decomposition and the accuracy, ease-of-use, and superiority of the proposed DL-based model for solenoid pump failure prognostics. Full article

(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control and Their Applications to Aerospace and Mechanical Systems)

► Show Figures

Figure 1

20 pages, 4170 KiB

Open AccessArticle

A Cost-Efficient MFCC-Based Fault Detection and Isolation Technology for Electromagnetic Pumps

by Ugochukwu Ejike Akpudo and Jang-Wook Hur

Electronics 2021, 10(4), 439; https://doi.org/10.3390/electronics10040439 - 10 Feb 2021

Cited by 22 | Viewed by 3644

Abstract

Fluid pumps serve critical purposes in hydraulic systems so their failure affects productivity, profitability, safety, etc. The need for proper condition monitoring and health assessment of these pumps cannot be overemphasized and this has resulted in extensive research studies on standard techniques for [...] Read more.

Fluid pumps serve critical purposes in hydraulic systems so their failure affects productivity, profitability, safety, etc. The need for proper condition monitoring and health assessment of these pumps cannot be overemphasized and this has resulted in extensive research studies on standard techniques for ensuring optimum fault detection and isolation (FDI) results for these pumps. Interestingly, mechanical vibrational signals reflect operating conditions and by exploring the robust time–frequency-domain feature extraction techniques, the underlying nonlinear characteristics can be captured for reliable fault diagnosis/condition assessment. This study is based on the use of vibrational signals for fault isolation of electromagnetic pumps. From the vibrational signals, Mel frequency cepstral coefficients (MFCCs), the first-order and the second-order differentials were extracted and the salient features selected by a rank-based recursive feature elimination (RFE) of uncorrelated features. The proposed framework was tested and validated on five VSC63A5 electromagnetic pumps at various fault conditions and isolated/classified using the Gaussian kernel SVM (SVM-RBF-RFE). Results show that the proposed feature selection approach is computationally cheaper and significantly improves diagnostics performance. In addition, the proposed framework yields a comparatively better diagnostics results on electromagnetic pumps in comparison with other diagnostics methods, hence a more reliable diagnostics tool for electromagnetic pumps. Full article

(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control and Their Applications to Aerospace and Mechanical Systems)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Fault Diagnosis and Fault-Tolerant Control and Their Applications to Aerospace and Mechanical Systems

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (8 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI