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Sensors and Signal Processing for Fault Diagnosis and Failure Prognosis of Means of Transport

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 17707

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Prof. Dr. Tomasz Figlus

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Faculty of Transport and Aviation Engineering, Silesian University of Technology, 8 Krasinskiego Street, 40-019 Katowice, Poland
Interests: signal processing; condition monitoring; vibration; noise; transport means
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Special Issue Information

Dear Colleagues,

I invite you to submit an original research paper to a special issue of the Sensors journal entitled “Sensors and Signal Processing for Fault Diagnosis and Failure Prognosis of Means of Transport”. Means of transport are required to exhibit high reliability and comfort of use. Vibration and noise signals of means of transport are valuable information about their technical condition. The use of different sensors and measurement methods as well as advanced signal processing methods enable early detection of wear and faults to means of transport. All this increases reliability and extends their failure-free operation, as well as allows scheduling repairs in advance. The development of measuring methods and equipment should be geared towards fast measurement in stationary and non-stationary operating conditions, even in hard-to-reach places. The signal recorded in this way requires advanced processing to be carried out in order to diagnose the damage occurring and to enable further prognosis. Improvement of existing solutions and development of new sensors, measurement methods and signal processing methods are the challenges and specific problems of the research work undertaken.

In this Special Issue, I encourage you to present new and original scientific papers in the area of simulation studies, laboratory and real tests of means of transport in the scope of:

- sensors and measurement devices,

- signal recording methods,

- advanced methods of processing vibration and noise signals,

- condition monitoring,

- diagnosing wear and faults,

- failure prognosis.

For further information please contact me by email.

Prof. Dr. Tomasz Figlus
Guest Editor

Manuscript Submission Information

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Keywords

Sensors
Condition monitoring
Signal processing
Vibration
Noise
Means of transport

Published Papers (6 papers)

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Research

21 pages, 6439 KiB

Open AccessArticle

Road Recognition Based on Vehicle Vibration Signal and Comfortable Speed Strategy Formulation Using ISA Algorithm

by Xiulai Wang, Zhun Cheng and Ningling Ma

Sensors 2022, 22(17), 6682; https://doi.org/10.3390/s22176682 - 03 Sep 2022

Cited by 2 | Viewed by 1592

Abstract

When a vehicle is being driven, it is excited by the road roughness and generates its own vibration. In order to improve the vehicle’s riding comfort and the physical–mental health of passengers in the vehicle, this paper proposes a formulation method for a comfortable speed strategy and the technical route of its application. According to international standard ISO 2631-1, the relationship between the weighted root-mean-square acceleration value and comfortable vehicle speed is analyzed. The simulation test platform of the road roughness signal and vehicle vibration signal is built by using the filtering white noise method and the second Lagrange equation through Matlab/Simulink. Combined with the simulation platform, this paper extracts seven characteristics with statistical properties from the time-domain signal and obtains 500 sample data. Random forest (RF), extreme learning machine (ELM), and radial basis function neural network (RBF-NN) are applied to identify roads. Two comfortable speed strategy formulation methods based on the improved simulated annealing (ISA) algorithm are proposed and compared according to the solution effect of each grade of comfortable speed. The results show that the simulated signals of each grade road roughness are accurate. Road recognition can be effectively carried out using the statistical characteristics of vehicle vibration acceleration signals. ELM has high recognition accuracy and fast execution speed. The ISA-II algorithm has a low solution error of comfortable speed and a low computation time. The comfortable speed of the research vehicle on different road grades showed a great difference. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Fault Diagnosis and Failure Prognosis of Means of Transport)

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12 pages, 5589 KiB

Open AccessArticle

Fault Identification in Membrane Structures Using the Hilbert Transforms

by Aleksandra Waszczuk-Młyńska, Adam Gałęzia and Radkowski Stanisław

Sensors 2022, 22(16), 6224; https://doi.org/10.3390/s22166224 - 19 Aug 2022

Cited by 2 | Viewed by 993

Abstract

Fault diagnostics present a crucial technical issue in the areas of both the condition monitoring of machines and the monitoring of structural health. The identification of faults at an early stage in their development has an immense effect on the safety of monitored structures. Correct identification allows for the monitoring of the development of faults and the choosing of optimal operation strategies. This article discusses a method of monitoring structural health, based on the application of the Hilbert transforms (GHT and FrHT) for detecting fault formations and their development in membrane structures. A signal resulting from the HT is analyzed using spectral analysis to identify features indicating the technical state. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Fault Diagnosis and Failure Prognosis of Means of Transport)

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24 pages, 5481 KiB

Open AccessArticle

A New De-Noising Method Based on Enhanced Time-Frequency Manifold and Kurtosis-Wavelet Dictionary for Rolling Bearing Fault Vibration Signal

by Qingbin Tong, Ziyu Liu, Feiyu Lu, Ziwei Feng and Qingzhu Wan

Sensors 2022, 22(16), 6108; https://doi.org/10.3390/s22166108 - 16 Aug 2022

Cited by 5 | Viewed by 1108

Abstract

The transient pulses caused by local faults of rolling bearings are an important measurement information for fault diagnosis. However, extracting transient pulses from complex nonstationary vibration signals with a large amount of background noise is challenging, especially in the early stage. To improve the anti-noise ability and detect incipient faults, a novel signal de-noising method based on enhanced time-frequency manifold (ETFM) and kurtosis-wavelet dictionary is proposed. First, to mine the high-dimensional features, the C-C method and Cao’s method are combined to determine the embedding dimension and delay time of phase space reconstruction. Second, the input parameters of the liner local tangent space arrangement (LLTSA) algorithm are determined by the grid search method based on Renyi entropy, and the dimension is reduced by manifold learning to obtain the ETFM with the highest time-frequency aggregation. Finally, a kurtosis-wavelet dictionary is constructed for selecting the best atom and eliminating the noise and reconstruct the defective signal. Actual simulations showed that the proposed method is more effective in noise suppression than traditional algorithms and that it can accurately reproduce the amplitude and phase information of the raw signal. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Fault Diagnosis and Failure Prognosis of Means of Transport)

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21 pages, 10186 KiB

Open AccessArticle

An Investigation of the Reliability of Different Types of Sensors in the Real-Time Vibration-Based Anomaly Inspection in Drone

by Mohamad Hazwan Mohd Ghazali and Wan Rahiman

Sensors 2022, 22(16), 6015; https://doi.org/10.3390/s22166015 - 12 Aug 2022

Cited by 22 | Viewed by 2877

Abstract

Early drone anomaly inspection is vital to ensure the drone’s safety and effectiveness. This process is often overlooked, especially by amateur drone pilots; however, some faulty conditions are difficult to notice by the naked eye or discover, even though the drone inspection process has been conducted; therefore, a real-time early drone inspection approach based on vibration data is proposed in this study. Firstly, the reliability of several microelectromechanical systems (MEMS) sensors, namely the ADXL335 accelerometer, ADXL 345 accelerometer, ADXL377 accelerometer, and SW420 vibration sensor in detecting faulty conditions, were tested and compared. The experimental results demonstrated that the vibration parameter measured using ADXL335 and ADXL345 accelerometers are the best choice as most of the faulty conditions can be detected, in contrast to other MEMS sensors. The output produced from the anomaly inspection algorithm is then converted to the “Healthy” or “Faulty” state, which is displayed in a mobile application for easy monitoring. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Fault Diagnosis and Failure Prognosis of Means of Transport)

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17 pages, 7902 KiB

Open AccessArticle

Damage Detection for Conveyor Belt Surface Based on Conditional Cycle Generative Adversarial Network

by Xiaoqiang Guo, Xinhua Liu, Grzegorz Królczyk, Maciej Sulowicz, Adam Glowacz, Paolo Gardoni and Zhixiong Li

Sensors 2022, 22(9), 3485; https://doi.org/10.3390/s22093485 - 03 May 2022

Cited by 17 | Viewed by 3580

Abstract

The belt conveyor is an essential piece of equipment in coal mining for coal transportation, and its stable operation is key to efficient production. Belt surface of the conveyor is vulnerable to foreign bodies which can be extremely destructive. In the past decades, much research and numerous approaches to inspect belt status have been proposed, and machine learning-based non-destructive testing (NDT) methods are becoming more and more popular. Deep learning (DL), as a branch of machine learning (ML), has been widely applied in data mining, natural language processing, pattern recognition, image processing, etc. Generative adversarial networks (GAN) are one of the deep learning methods based on generative models and have been proved to be of great potential. In this paper, a novel multi-classification conditional CycleGAN (MCC-CycleGAN) method is proposed to generate and discriminate surface images of damages of conveyor belt. A novel architecture of improved CycleGAN is designed to enhance the classification performance using a limited capacity images dataset. Experimental results show that the proposed deep learning network can generate realistic belt surface images with defects and efficiently classify different damaged images of the conveyor belt surface. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Fault Diagnosis and Failure Prognosis of Means of Transport)

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26 pages, 13257 KiB

Open AccessFeature PaperArticle

Condition Monitoring of Railway Crossing Geometry via Measured and Simulated Track Responses

by Marko D. G. Milosevic, Björn A. Pålsson, Arne Nissen, Jens C. O. Nielsen and Håkan Johansson

Sensors 2022, 22(3), 1012; https://doi.org/10.3390/s22031012 - 28 Jan 2022

Cited by 14 | Viewed by 6584

Abstract

This paper presents methods for continuous condition monitoring of railway switches and crossings (S&C, turnout) via sleeper-mounted accelerometers at the crossing transition. The methods are developed from concurrently measured sleeper accelerations and scanned crossing geometries from six in situ crossing panels. These measurements combined with a multi-body simulation (MBS) model with a structural track model and implemented scanned crossing geometries are used to derive the link between the crossing geometry condition and the resulting track excitation. From this analysis, a crossing condition indicator

C_{λ_{1} - λ_{2}, γ}

is proposed. The indicator is defined as the root mean square (RMS) of a track response signal γ that has been band-passed between frequencies corresponding to track deformation wavelength bounds of

λ_{1}

and

λ_{2}

for the vehicle passing speed (f = v/

λ

). In this way, the indicator ignores the quasi-static track response with wavelengths predominantly above

λ_{1}

and targets the dynamic track response caused by the kinematic wheel-crossing interaction governed by the crossing geometry. For the studied crossing panels, the indicator

C_{1 - 0.2 m, γ}

(

λ_{1} = 1

and

λ_{2} = 0.2

) was evaluated for γ = u, v, or a as in displacements, velocities, and accelerations, respectively. It is shown that this condition indicator has a strong correlation with vertical wheel–rail contact forces that is sustained for various track conditions. Further, model calibrations were performed to measured sleeper displacements for the six investigated crossing panels. The calibrated models show (1) a good agreement between measured and simulated sleeper displacements for the lower frequency quasi-static track response and (2) improved agreement for the dynamic track response at higher frequencies. The calibration also improved the agreement between measurements and simulation for the crossing condition indicator demonstrating the value of model calibration for condition monitoring purposes. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Fault Diagnosis and Failure Prognosis of Means of Transport)

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