Research

15 pages, 1872 KiB

Open AccessArticle

Performance Assessment of Pneumatic-Driven Automatic Valves to Improve Pipeline Fault Detection Procedure by Fast Transient Tests

by Francesco Castellani, Caterina Capponi, Bruno Brunone, Matteo Vedovelli and Silvia Meniconi

Sensors 2024, 24(6), 1825; https://doi.org/10.3390/s24061825 - 12 Mar 2024

Viewed by 469

Abstract

The use of fast transients for fault detection in long transmission networks makes the generation of controlled transients crucial. In order to maximise the information that can be extracted from the measured pressure time history (pressure signal), the transients must meet certain requirements. [...] Read more.

The use of fast transients for fault detection in long transmission networks makes the generation of controlled transients crucial. In order to maximise the information that can be extracted from the measured pressure time history (pressure signal), the transients must meet certain requirements. In particular, the manoeuvre that generates the transient must be fast and repeatable, and must produce a pressure wave that is as sharp as possible, without spurious pressure oscillations. This implies the use of small-diameter valves and often pneumatically operated automatic valves. In the present work, experimental transient tests are carried out at the Water Engineering Laboratory (WEL) of the University of Perugia using a butterfly valve and a ball pneumatic-driven valve to generate pressure waves in a pressurised copper pipe. A camera is used to monitor the valve displacement, while the pressure is measured by a pressure transducer close to the downstream end of the pipe where the pneumatic valve is installed. The experimental data are analysed to characterise the valve performance and to compare the two geometries in terms of valve closing dynamics, the sharpness of the generated pressure wave and the stability of the pressure time history. The present work demonstrates how the proposed approach can be very effective in easily characterising the transient dynamics. Full article

(This article belongs to the Special Issue Sensors and Methods for Diagnostics and Early Fault Detection)

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27 pages, 25916 KiB

Open AccessArticle

A Hybrid System for Defect Detection on Rail Lines through the Fusion of Object and Context Information

by Alexey Zhukov, Alain Rivero, Jenny Benois-Pineau, Akka Zemmari and Mohamed Mosbah

Sensors 2024, 24(4), 1171; https://doi.org/10.3390/s24041171 - 10 Feb 2024

Viewed by 638

Abstract

Defect detection on rail lines is essential for ensuring safe and efficient transportation. Current image analysis methods with deep neural networks (DNNs) for defect detection often focus on the defects themselves while ignoring the related context. In this work, we propose a fusion [...] Read more.

Defect detection on rail lines is essential for ensuring safe and efficient transportation. Current image analysis methods with deep neural networks (DNNs) for defect detection often focus on the defects themselves while ignoring the related context. In this work, we propose a fusion model that combines both a targeted defect search and a context analysis, which is seen as a multimodal fusion task. Our model performs rule-based decision-level fusion, merging the confidence scores of multiple individual models to classify rail-line defects. We call the model “hybrid” in the sense that it is composed of supervised learning components and rule-based fusion. We first propose an improvement to existing vision-based defect detection methods by incorporating a convolutional block attention module (CBAM) in the you only look once (YOLO) versions 5 (YOLOv5) and 8 (YOLOv8) architectures for the detection of defects and contextual image elements. This attention module is applied at different detection scales. The domain-knowledge rules are applied to fuse the detection results. Our method demonstrates improvements over baseline models in vision-based defect detection. The model is open for the integration of modalities other than an image, e.g., sound and accelerometer data. Full article

(This article belongs to the Special Issue Sensors and Methods for Diagnostics and Early Fault Detection)

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22 pages, 14465 KiB

Open AccessArticle

CHP Engine Anomaly Detection Based on Parallel CNN-LSTM with Residual Blocks and Attention

by Won Hee Chung, Yeong Hyeon Gu and Seong Joon Yoo

Sensors 2023, 23(21), 8746; https://doi.org/10.3390/s23218746 - 26 Oct 2023

Viewed by 772

Abstract

The extreme operating environment of the combined heat and power (CHP) engine is likely to cause anomalies and defects, which can lead to engine failure; thus, detecting engine anomalies is essential. In this study, we propose a parallel convolutional neural network–long short-term memory [...] Read more.

The extreme operating environment of the combined heat and power (CHP) engine is likely to cause anomalies and defects, which can lead to engine failure; thus, detecting engine anomalies is essential. In this study, we propose a parallel convolutional neural network–long short-term memory (CNN-LSTM) residual blocks attention (PCLRA) anomaly detection model with engine sensor data. To our knowledge, this is the first time that parallel CNN-LSTM-based networks have been used in the field of CHP engine anomaly detection. In PCLRA, spatiotemporal features are extracted via CNN-LSTM in parallel and the information loss is compensated using the residual blocks and attention mechanism. The performance of PCLRA is compared with various hybrid models for 15 cases. First, the performances of serial and parallel models are compared. In addition, we evaluated the contributions of the residual blocks and attention mechanism to the performance of the CNN–LSTM hybrid model. The results indicate that PCLRA achieves the best performance, with a macro f1 score (mean ± standard deviation) of 0.951 ± 0.033, an anomaly f1 score of 0.903 ± 0.064, and an accuracy of 0.999 ± 0.002. We expect that the energy efficiency and safety of CHP engines can be improved by applying the PCLRA anomaly detection model. Full article

(This article belongs to the Special Issue Sensors and Methods for Diagnostics and Early Fault Detection)

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22 pages, 7270 KiB

Open AccessArticle

Multi-Damage Detection in Composite Space Structures via Deep Learning

by Federica Angeletti, Paolo Gasbarri, Massimo Panella and Antonello Rosato

Sensors 2023, 23(17), 7515; https://doi.org/10.3390/s23177515 - 29 Aug 2023

Cited by 1 | Viewed by 1136

Abstract

The diagnostics of environmentally induced damages in composite structures plays a critical role for ensuring the operational safety of space platforms. Recently, spacecraft have been equipped with lightweight and very large substructures, such as antennas and solar panels, to meet the performance demands [...] Read more.

The diagnostics of environmentally induced damages in composite structures plays a critical role for ensuring the operational safety of space platforms. Recently, spacecraft have been equipped with lightweight and very large substructures, such as antennas and solar panels, to meet the performance demands of modern payloads and scientific instruments. Due to their large surface, these components are more susceptible to impacts from orbital debris compared to other satellite locations. However, the detection of debris-induced damages still proves challenging in large structures due to minimal alterations in the spacecraft global dynamics and calls for advanced structural health monitoring solutions. To address this issue, a data-driven methodology using Long Short-Term Memory (LSTM) networks is applied here to the case of damaged solar arrays. Finite element models of the solar panels are used to reproduce damage locations, which are selected based on the most critical risk areas in the structures. The modal parameters of the healthy and damaged arrays are extracted to build the governing equations of the flexible spacecraft. Standard attitude manoeuvres are simulated to generate two datasets, one including local accelerations and the other consisting of piezoelectric voltages, both measured in specific locations of the structure. The LSTM architecture is then trained by associating each sensed time series with the corresponding damage label. The performance of the deep learning approach is assessed, and a comparison is presented between the accuracy of the two distinct sets of sensors: accelerometers and piezoelectric patches. In both cases, the framework proved effective in promptly identifying the location of damaged elements within limited measured time samples. Full article

(This article belongs to the Special Issue Sensors and Methods for Diagnostics and Early Fault Detection)

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25 pages, 4191 KiB

Open AccessArticle

PEMFCs Model-Based Fault Diagnosis: A Proposal Based on Virtual and Real Sensors Data Fusion

by Eduardo Ariza, Antonio Correcher and Carlos Vargas-Salgado

Sensors 2023, 23(17), 7383; https://doi.org/10.3390/s23177383 - 24 Aug 2023

Cited by 1 | Viewed by 836

Abstract

Proton Exchange Membrane Fuel Cells (PEMFCs) are critical components in renewable hybrid systems, demanding reliable fault diagnosis to ensure optimal performance and prevent costly damages. This study presents a novel model-based fault diagnosis algorithm for commercial hydrogen fuel cells using LabView. Our research [...] Read more.

Proton Exchange Membrane Fuel Cells (PEMFCs) are critical components in renewable hybrid systems, demanding reliable fault diagnosis to ensure optimal performance and prevent costly damages. This study presents a novel model-based fault diagnosis algorithm for commercial hydrogen fuel cells using LabView. Our research focused on power generation and storage using hydrogen fuel cells. The proposed algorithm accurately detects and isolates the most common faults in PEMFCs by combining virtual and real sensor data fusion. The fault diagnosis process began with simulating faults using a validated mathematical model and manipulating selected input signals. A statistical analysis of 12 residues from each fault resulted in a comprehensive fault matrix, capturing the unique fault signatures. The algorithm successfully identified and isolated 14 distinct faults, demonstrating its effectiveness in enhancing reliability and preventing performance deterioration or system shutdown in hydrogen fuel cell-based power generation systems. Full article

(This article belongs to the Special Issue Sensors and Methods for Diagnostics and Early Fault Detection)

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15 pages, 3686 KiB

Open AccessArticle

AI-Enabled IoT Framework for Leakage Detection and Its Consequence Prediction during External Transportation of LPG

by Amiya Dash, Shuvabrata Bandopadhay, Soumya Ranjan Samal and Vladimir Poulkov

Sensors 2023, 23(14), 6473; https://doi.org/10.3390/s23146473 - 17 Jul 2023

Cited by 1 | Viewed by 1445

Abstract

An accident during the transport of liquefied petroleum gas (LPG) via a tanker vehicle leads to the leakage of a flammable substance, causing devastation. In such a situation, the appropriate action with the shortest possible delay can minimize subsequent losses. However, the decision-making [...] Read more.

An accident during the transport of liquefied petroleum gas (LPG) via a tanker vehicle leads to the leakage of a flammable substance, causing devastation. In such a situation, the appropriate action with the shortest possible delay can minimize subsequent losses. However, the decision-making mechanism remains unable to detect the occurrence of an accident and evaluate its extent within the critical time. This paper proposes an automatic framework for leakage detection and its consequence prediction during the external transportation of LPG using artificial intelligence (AI) and the internet of things (IoT). An AI model is developed to predict the probable consequences of the accident in terms of the diameter of risk contours. An IoT framework is proposed in which the developed AI model is deployed in the edge device to detect any leakage of gas during transportation, to predict its probable consequences, and to report it to the remotely located disaster management team for initiating appropriate action. A prototype of the proposed model is built and its performance is successfully tested. The proposed solution would significantly help to identify efficient disaster management techniques by allowing for quick leakage detection and the prediction of its probable consequences. Full article

(This article belongs to the Special Issue Sensors and Methods for Diagnostics and Early Fault Detection)

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20 pages, 1114 KiB

Open AccessArticle

Correlative Method for Diagnosing Gas-Turbine Tribological Systems

by Maciej Deliś, Sylwester Kłysz and Radoslaw Przysowa

Sensors 2023, 23(12), 5738; https://doi.org/10.3390/s23125738 - 20 Jun 2023

Viewed by 936

Abstract

Lubricated tribosystems such as main-shaft bearings in gas turbines have been successfully diagnosed by oil sampling for many years. In practice, the interpretation of wear debris analysis results can pose a challenge due to the intricate structure of power transmission systems and the [...] Read more.

Lubricated tribosystems such as main-shaft bearings in gas turbines have been successfully diagnosed by oil sampling for many years. In practice, the interpretation of wear debris analysis results can pose a challenge due to the intricate structure of power transmission systems and the varying degrees of sensitivity among test methods. In this work, oil samples acquired from the fleet of M601T turboprop engines were tested with optical emission spectrometry and analyzed with a correlative model. Customized alarm limits were determined for iron by binning aluminum and zinc concentration into four levels. Two-way analysis of variance (ANOVA) with interaction analysis and post hoc tests was carried out to study the impact of aluminum and zinc concentration on iron concentration. A strong correlation between iron and aluminum, as well as a weaker but still statistically significant correlation between iron and zinc, was observed. When the model was applied to evaluate a selected engine, deviations of iron concentration from the established limits indicated accelerated wear long before the occurrence of critical damage. Thanks to ANOVA, the assessment of engine health was based on a statistically proven correlation between the values of the dependent variable and the classifying factors. Full article

(This article belongs to the Special Issue Sensors and Methods for Diagnostics and Early Fault Detection)

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39 pages, 27207 KiB

Open AccessArticle

Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time–Frequency-Based Features and Deep Learning Models

by Sameer Sayyad, Satish Kumar, Arunkumar Bongale, Ketan Kotecha and Ajith Abraham

Sensors 2023, 23(12), 5659; https://doi.org/10.3390/s23125659 - 17 Jun 2023

Cited by 4 | Viewed by 1590

Abstract

The milling machine serves an important role in manufacturing because of its versatility in machining. The cutting tool is a critical component of machining because it is responsible for machining accuracy and surface finishing, impacting industrial productivity. Monitoring the cutting tool’s life is [...] Read more.

The milling machine serves an important role in manufacturing because of its versatility in machining. The cutting tool is a critical component of machining because it is responsible for machining accuracy and surface finishing, impacting industrial productivity. Monitoring the cutting tool’s life is essential to avoid machining downtime caused due to tool wear. To prevent the unplanned downtime of the machine and to utilize the maximum life of the cutting tool, the accurate prediction of the remaining useful life (RUL) cutting tool is essential. Different artificial intelligence (AI) techniques estimate the RUL of cutting tools in milling operations with improved prediction accuracy. The IEEE NUAA Ideahouse dataset has been used in this paper for the RUL estimation of the milling cutter. The accuracy of the prediction is based on the quality of feature engineering performed on the unprocessed data. Feature extraction is a crucial phase in RUL prediction. In this work, the authors considers the time–frequency domain (TFD) features such as short-time Fourier-transform (STFT) and different wavelet transforms (WT) along with deep learning (DL) models such as long short-term memory (LSTM), different variants of LSTN, convolutional neural network (CNN), and hybrid models that are a combination of CCN with LSTM variants for RUL estimation. The TFD feature extraction with LSTM variants and hybrid models performs well for the milling cutting tool RUL estimation. Full article

(This article belongs to the Special Issue Sensors and Methods for Diagnostics and Early Fault Detection)

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16 pages, 5168 KiB

Open AccessArticle

A Support Vector Machine-Based Approach for Bolt Loosening Monitoring in Industrial Customized Vehicles

by Simone Carone, Giovanni Pappalettera, Caterina Casavola, Simone De Carolis and Leonardo Soria

Sensors 2023, 23(11), 5345; https://doi.org/10.3390/s23115345 - 05 Jun 2023

Cited by 6 | Viewed by 1290

Abstract

Machine learning techniques have progressively emerged as important and reliable tools that, when combined with machine condition monitoring, can diagnose faults with even superior performance than other condition-based monitoring approaches. Furthermore, statistical or model-based approaches are often not applicable in industrial environments with [...] Read more.

Machine learning techniques have progressively emerged as important and reliable tools that, when combined with machine condition monitoring, can diagnose faults with even superior performance than other condition-based monitoring approaches. Furthermore, statistical or model-based approaches are often not applicable in industrial environments with a high degree of customization of equipment and machines. Structures such as bolted joints are a key part of the industry; therefore, monitoring their health is critical to maintaining structural integrity. Despite this, there has been little research on the detection of bolt loosening in rotating joints. In this study, vibration-based detection of bolt loosening in a rotating joint of a custom sewer cleaning vehicle transmission was performed using support vector machines (SVM). Different failures were analyzed for various vehicle operating conditions. Several classifiers were trained to evaluate the influence of the number and location of accelerometers used and to determine the best approach between specific models for each operating condition or a single model for all cases. The results showed that using a single SVM model with data from four accelerometers mounted both upstream and downstream of the bolted joint resulted in more reliable fault detection, with an overall accuracy of 92.4%. Full article

(This article belongs to the Special Issue Sensors and Methods for Diagnostics and Early Fault Detection)

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24 pages, 8526 KiB

Open AccessArticle

Development and Validation of a Low-Cost Device for Real-Time Detection of Fatigue Damage of Structures Subjected to Vibrations

by Agnese Staffa, Massimiliano Palmieri, Giulia Morettini, Guido Zucca, Francesco Crocetti and Filippo Cianetti

Sensors 2023, 23(11), 5143; https://doi.org/10.3390/s23115143 - 28 May 2023

Cited by 2 | Viewed by 856

Abstract

This paper presents the development and validation of a low-cost device for real-time detection of fatigue damage of structures subjected to vibrations. The device consists of an hardware and signal processing algorithm to detect and monitor variations in the structural response due to [...] Read more.

This paper presents the development and validation of a low-cost device for real-time detection of fatigue damage of structures subjected to vibrations. The device consists of an hardware and signal processing algorithm to detect and monitor variations in the structural response due to damage accumulation. The effectiveness of the device is demonstrated through experimental validation on a simple Y-shaped specimen subjected to fatigue loading. The results show that the device can accurately detect structural damage and provide real-time feedback on the health status of the structure. The low-cost and easy-to-implement nature of the device makes it promising for use in structural health monitoring applications in various industrial sectors. Full article

(This article belongs to the Special Issue Sensors and Methods for Diagnostics and Early Fault Detection)

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24 pages, 6060 KiB

Open AccessArticle

Method of Failure Diagnostics to Linear Rolling Guides in Handling Machines

by Radka Jírová, Lubomír Pešík, Lucia Žuľová and Robert Grega

Sensors 2023, 23(7), 3770; https://doi.org/10.3390/s23073770 - 06 Apr 2023

Cited by 1 | Viewed by 1060

Abstract

Linear rolling guides, used in production machines for the realisation of linear motion, demand in industrial practice early damage identification to prevent production outages and losses. Therefore, the article aims for early damage diagnostics that use the principle of a load-free diagnostic part [...] Read more.

Linear rolling guides, used in production machines for the realisation of linear motion, demand in industrial practice early damage identification to prevent production outages and losses. Therefore, the article aims for early damage diagnostics that use the principle of a load-free diagnostic part integrated into the carriage of the linear rolling guide. This principle was employed for developing an innovative method of damage identification to a guiding profile or rolling elements. The proposed innovative method is based on analysing vibration acceleration measured on the diagnostic part in the context of carriage position. In addition, a unique connection of an acceleration sensor to the diagnostic part through a mechanical component with defined parameters of stiffness and mass was designed. The innovative method was verified by laboratory testing on a designed functional sample of the diagnostic system. The computed reliability of the proposed diagnostic method reached 98%. Full article

(This article belongs to the Special Issue Sensors and Methods for Diagnostics and Early Fault Detection)

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22 pages, 835 KiB

Open AccessArticle

State-Space Model for Arrival Time Simulations and Methodology for Offline Blade Tip-Timing Software Characterization

by Tommaso Tocci, Lorenzo Capponi, Gianluca Rossi, Roberto Marsili and Marco Marrazzo

Sensors 2023, 23(5), 2600; https://doi.org/10.3390/s23052600 - 26 Feb 2023

Cited by 2 | Viewed by 1466

Abstract

Blade tip-timing is an extensively used technique for measuring blade vibrations in turbine and compressor stages; it is one of the preferred techniques used for characterizing their dynamic behaviors using non-contact probes. Typically, arrival time signals are acquired and processed by a dedicated [...] Read more.

Blade tip-timing is an extensively used technique for measuring blade vibrations in turbine and compressor stages; it is one of the preferred techniques used for characterizing their dynamic behaviors using non-contact probes. Typically, arrival time signals are acquired and processed by a dedicated measurement system. Performing a sensitivity analysis on the data processing parameters is essential for the proper design of tip-timing test campaigns. This study proposes a mathematical model for generating synthetic tip-timing signals, descriptive of specific test conditions. The generated signals were used as the controlled input for a thorough characterization of post-processing software for tip-timing analysis. This work represents the first step in quantifying the uncertainty introduced by tip-timing analysis software into user measurements. The proposed methodology can also offer essential information for further sensitivity studies on parameters that influence the accuracy of data analysis during testing. Full article

(This article belongs to the Special Issue Sensors and Methods for Diagnostics and Early Fault Detection)

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16 pages, 8995 KiB

Open AccessArticle

A Smart System for an Assessment of the Remaining Useful Life of Ball Bearings by Applying Chaos-Based Health Indicators and a Self-Selective Regression Model

by Shih-Yu Li, Hao-An Li, Lap-Mou Tam and Chin-Sheng Chen

Sensors 2023, 23(3), 1267; https://doi.org/10.3390/s23031267 - 22 Jan 2023

Cited by 3 | Viewed by 1542

Abstract

Bearings are the most commonly used components in rotating machines and the ability to diagnose their faults and predict their remaining useful life (RUL) is critical for system maintenance. This paper proposes a smart system combined with a regression model to predict the [...] Read more.

Bearings are the most commonly used components in rotating machines and the ability to diagnose their faults and predict their remaining useful life (RUL) is critical for system maintenance. This paper proposes a smart system combined with a regression model to predict the RUL of bearings. The method converts the azimuth signal through low-pass filtering (LPF) and a chaotic mapping system, and uses Euclidean feature values (EFVs) to extract features in order to construct useful health indicators (HIs). In fault detection, the iterative cumulative moving average (ICMA) is used to smooth the HIs, and the Euclidean norm is used to find the time-to-start prediction (TSP). In terms of prediction, this paper uses a self-selective regression model to select the most suitable regression model to predict the RUL of the bearing. The dataset provided by the Center for Intelligent Maintenance Systems (IMS) is applied for performance evaluation; in comparison with previous research, better prediction results can be achieved by applying the proposed smart assessment system. The proposed system is also applied to the PRONOSTIA (also called FEMTO-ST) bearing dataset in this paper, demonstrating that acceptable prediction performance can be obtained. Full article

(This article belongs to the Special Issue Sensors and Methods for Diagnostics and Early Fault Detection)

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12 pages, 628 KiB

Open AccessArticle

Experimental Investigation on Hardware and Triggering Effect in Tip-Timing Measurement Uncertainty

by Lorenzo Capponi, Tommaso Tocci, Marco Marrazzo, Roberto Marsili and Gianluca Rossi

Sensors 2023, 23(3), 1129; https://doi.org/10.3390/s23031129 - 18 Jan 2023

Cited by 4 | Viewed by 1132

Abstract

Non-destructive testing for structural health monitoring is becoming progressively important for gas turbine manufacturers. As several techniques for diagnostics and condition-based maintenance have been developed over the years, the tip-timing approach is one of the preferred approaches for characterizing the dynamic behavior of [...] Read more.

Non-destructive testing for structural health monitoring is becoming progressively important for gas turbine manufacturers. As several techniques for diagnostics and condition-based maintenance have been developed over the years, the tip-timing approach is one of the preferred approaches for characterizing the dynamic behavior of turbine blades using non-contact probes. This experimental work investigates the uncertainty of the time-of-arrival of a Blade Tip-Timing measurement system, a fundamental requirement for numerical and aeromechanical modeling validation. The study is applied to both the measurement setup and the data processing procedure of a generic commercial measurement system. The influence of electronic components and signal processing on the tip-timing uncertainty is determined under different operating conditions. Full article

(This article belongs to the Special Issue Sensors and Methods for Diagnostics and Early Fault Detection)

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