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Condition Monitoring of Mechanical Transmission Systems

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

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 11234

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Special Issue Editors

Dr. Ke Feng

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Guest Editor

School of Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Interests: machine condition monitoring; vibration analysis; fault diagnosis and prognostics; digital twin; dynamic; signal processing
Special Issues, Collections and Topics in MDPI journals

Dr. Qing Ni

E-Mail Website
Guest Editor

School of Mechanical and Mechatronic Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
Interests: fault diagnosis; RUL prediction; vibration analysis; signal processing; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Ruyi Huang

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Guest Editor

Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 510641, China
Interests: intelligent fault diagnosis; prognostics and health management; multisensory data/information fusion technology for condition-based maintenance

Prof. Dr. Zhixiong Li

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Guest Editor

Yonsei Frontier Lab, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
Interests: dynamic system modeling and optimization; sensor data analysis; machine learning and applications
Special Issues, Collections and Topics in MDPI journals

Dr. Yongchao Zhang

E-Mail Website
Guest Editor

School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110004, China
Interests: intelligent fault diagnosis; prognostics and health management; transfer learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mechanical transmission systems are widely used in various industrial applications, such as wind turbines, ships, vehicles, and advanced manufacturing. However, mechanical transmission systems usually operate in harsh working environments, making some critical components (such as bearings, gears, shafts, etc.) prone to fatigue and failure. The fatigue and failure of these components can cause a sudden shutdown of the transmission system and result in unexpected economic loss and even serious accidents. Therefore, it is necessary to monitor the health status of mechanical transmission systems so that proper maintenance strategies can be scheduled in advance, which can ensure the safe operation of machinery and significantly improve the industry practices.

Vibration analysis is a prevalent tool that can be used to monitor the operating conditions of mechanical transmission systems. A machine in a healthy state has a certain vibration signature. Fatigue and failure development can change this signature in a way that can be related to the fault. Thus, vibration analysis has been recognized as an effective approach to monitor the conditions of mechanical transmission systems.

The Special Issue focuses on advanced and innovative vibration analysis techniques that can be used to monitor the health condition of mechanical transmission systems, including vital components such as gears, bearings, shafts, couplings, and motors.

Potential topics include but are not limited to:

Vibration-based machinery diagnostics;
Dynamic analysis for condition monitoring;
Digital-twin-based fault diagnostics and prognostics;
Remaining useful life prediction of the mechanical transmission system using advanced vibration analysis algorithms;
Machinery fault diagnostics under non-stationary operating conditions;
Fatigue analysis of mechanical transmission systems;
Artificial-intelligence-based fault diagnostics and prognostics.

Dr. Ke Feng
Dr. Qing Ni
Dr. Ruyi Huang
Prof. Dr. Zhixiong Li
Dr. Yongchao Zhang
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

gearbox
bearing
signal processing
fault diagnosis
fault prognosis
RUL prediction
condition monitoring

Published Papers (5 papers)

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Research

17 pages, 6897 KiB

Open AccessArticle

Surface Quality Improvement for Ultrasonic-Assisted Inner Diameter Sawing with Six-Axis Force Sensors

by Jinghe Zhao, Lulu Wang, Bo Jiang, Yongchen Pei and Huiqi Lu

Sensors 2023, 23(14), 6444; https://doi.org/10.3390/s23146444 - 16 Jul 2023

Cited by 1 | Viewed by 1052

Abstract

Ultrasonic-assisted inner diameter machining is a slicing method for hard and brittle materials. During this process, the sawing force is the main factor affecting the workpiece surface quality and tool life. Therefore, based on indentation fracture mechanics, a theoretical model of the cutting force of an ultrasound-assisted inner diameter saw is established in this paper for surface quality improvement. The cutting experiment was carried out with alumina ceramics (99%) as an exemplar of hard and brittle material. A six-axis force sensor was used to measure the sawing force in the experiment. The correctness of the theoretical model was verified by comparing the theoretical modeling with the actual cutting force, and the influence of machining parameters on the normal sawing force was evaluated. The experimental results showed that the ultrasonic-assisted cutting force model based on the six-axis force sensor proposed in this paper was more accurate. Compared with the regular tetrahedral abrasive model, the mean value and variance of the proposed model’s force prediction error were reduced by 5.08% and 2.56%. Furthermore, by using the proposed model, the sawing processing parameters could be updated to improve the slice surface quality from a roughness Sa value of 1.534 µm to 1.129 µm. The proposed model provides guidance for the selection of process parameters and can improve processing efficiency and quality in subsequent real-world production. Full article

(This article belongs to the Special Issue Condition Monitoring of Mechanical Transmission Systems)

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13 pages, 4957 KiB

Open AccessArticle

Fault Diagnosing of Cycloidal Gear Reducer Using Statistical Features of Vibration Signal and Multifractal Spectra

by Iwona Komorska, Krzysztof Olejarczyk, Andrzej Puchalski, Marcin Wikło and Zbigniew Wołczyński

Sensors 2023, 23(3), 1645; https://doi.org/10.3390/s23031645 - 02 Feb 2023

Cited by 5 | Viewed by 3803

Abstract

The article presents a method for diagnosing cycloidal gear damage on a laboratory stand. The damage was simulated by removing the sliding sleeves from two adjacent external pins of the cycloidal gearbox. Damage to the sliding sleeves may occur under operating conditions and can lead to the destruction of the gear unit. Hence, early detection is essential. Signals from torque sensors, rotational speed sensors and vibration acceleration sensors of input and output shafts for various rotational speeds and transmission loads were recorded. The frequency analysis of these signals was carried out. Due to the fluctuation of the rotational speed, the frequency spectrum gives an approximate picture and is not useful in detecting this type of damage. The statistical characteristics of the signal were determined. However, only statistical moments of higher orders, such as kurtosis, are sensitive to the tested damage. Therefore, the use of multifractal analysis of the vibration signal using the wavelet leader method (WLMF) was considered. Then log-cumulants of the multifractal spectrum were selected as the new signal features. Full article

(This article belongs to the Special Issue Condition Monitoring of Mechanical Transmission Systems)

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38 pages, 15638 KiB

Open AccessArticle

On the Behaviour of an AC Induction Motor as Sensor for Condition Monitoring of Driven Rotary Machines

by Mihaita Horodinca, Neculai-Eduard Bumbu, Dragos-Florin Chitariu, Adriana Munteanu, Catalin-Gabriel Dumitras, Florin Negoescu and Constantin-Gheorghe Mihai

Sensors 2023, 23(1), 488; https://doi.org/10.3390/s23010488 - 02 Jan 2023

Cited by 2 | Viewed by 2087

Abstract

This paper presents some advances in condition monitoring for rotary machines (particularly for a lathe headstock gearbox) running idle with a constant speed, based on the behaviour of a driving three-phase AC asynchronous induction motor used as a sensor of the mechanical power via the absorbed electrical power. The majority of the variable phenomena involved in this condition monitoring are periodical (machines having rotary parts) and should be mechanically supplied through a variable electrical power absorbed by a motor with periodical components (having frequencies equal to the rotational frequency of the machine parts). The paper proposes some signal processing and analysis methods for the variable part of the absorbed electrical power (or its constituents: active and instantaneous power, instantaneous current, power factor, etc.) in order to achieve a description of these periodical constituents, each one often described as a sum of sinusoidal components with a fundamental and some harmonics. In testing these methods, the paper confirms the hypothesis that the evolution of the electrical power (instantaneous and active) has a predominantly deterministic character. Two main signal analysis methods were used, with good, comparable results: the fast Fourier transform of short and long signal sequences (for the frequency domain) and the curve fitting estimation (in the time domain). The determination of the amplitude, frequency and phase at origin of time for each of these components helps to describe the condition (normal or abnormal) of the machine parts. Several achievements confirm the viability of this study: a characterization of a flat driving belt condition and a beating power phenomenon generated by two rotary shafts inside the gearbox. For comparison purposes, the same signal analysis methods were applied to describe the evolution of the vibration signal and the instantaneous angular speed signal at the gearbox output spindle. Many similarities in behaviour among certain mechanical parts (including their electrical power, vibration and instantaneous angular speed) were highlighted. Full article

(This article belongs to the Special Issue Condition Monitoring of Mechanical Transmission Systems)

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24 pages, 6215 KiB

Open AccessArticle

Health Status Assessment of Diesel Engine Valve Clearance Based on BFA-BOA-VMD Adaptive Noise Reduction and Multi-Channel Information Fusion

by Yangshuo Liu, Jianshe Kang, Liang Wen, Yunjie Bai and Chiming Guo

Sensors 2022, 22(21), 8129; https://doi.org/10.3390/s22218129 - 24 Oct 2022

Cited by 4 | Viewed by 1443

Abstract

Regarding the problem of the valve gap health status being difficult to assess due to the complex composition of the condition monitoring signal during the operation of the diesel engine, this paper proposes an adaptive noise reduction and multi-channel information fusion method for the health status assessment of diesel engine valve clearance. For the problem of missing fault information of single-channel sensors in condition monitoring, we built a diesel engine valve clearance preset simulation test bench and constructed a multi-sensor acquisition system to realize the acquisition of diesel engine multi-dimensional cylinder head signals. At the same time, for the problem of poor adaptability of most signal analysis methods, the improved butterfly optimization algorithm by the bacterial foraging algorithm was adopted to adaptively optimize the key parameter for variational mode decomposition, with discrete entropy as the fitness value. Then, to reduce the uncertainty of artificially selecting fault characteristics, the characteristic parameters with a higher recognition degree of diesel engine signal were selected through characteristic sensitivity analysis. To achieve an effective dimensionality reduction integration of multi-channel features, a stacked sparse autoencoder was used to achieve deep fusion of the multi-dimensional feature values. Finally, the feature samples were entered into the constructed one-dimensional convolutional neural network with a four-layer parameter space for training to realize the health status assessment of the diesel engine. In addition, we verified the effectiveness of the method by carrying out valve degradation simulation experiments on the diesel engine test bench. Experimental results show that, compared with other common evaluation methods, the method used in this paper has a better health state evaluation effect. Full article

(This article belongs to the Special Issue Condition Monitoring of Mechanical Transmission Systems)

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31 pages, 7223 KiB

Open AccessArticle

Opportunistic Maintenance Strategy for Complex Equipment with a Genetic Algorithm Considering Failure Dependence: A Two-Dimensional Warranty Perspective

by Enzhi Dong, Tielu Gao, Zhonghua Cheng, Rongcai Wang and Yongsheng Bai

Sensors 2022, 22(18), 6801; https://doi.org/10.3390/s22186801 - 08 Sep 2022

Cited by 7 | Viewed by 1994

Abstract

Complex two-dimensional warranty equipment is usually composed of many multi-component systems, which include several key components. During the warranty period, conducting maintenance according to the preventive maintenance plan of each component will increase the warranty costs. Opportunistic maintenance is an effective approach to combine the preventive maintenance of each individual component, which can reduce the warranty cost and improve the system availability. This study explored the optimal opportunistic maintenance scheme of multi-component systems. Firstly, the failure rate model and reliability evaluation model of the multi-component system considering failure dependence were established. Secondly, the preventive maintenance plan of each individual component was determined, with the goal of obtaining the lowest warranty cost per unit time in the component life cycle. Thirdly, the preventive maintenance work of each individual component was combined, and the two-dimensional warranty cost model of the multi-component system was established according to the reliability threshold when performing opportunistic maintenance. In the experimental verification and result analysis, the genetic algorithm was used to find the optimal opportunistic maintenance scheme for the power transmission device. The comparative analysis results show that the opportunistic maintenance scheme reduced the warranty cost by 5.5% and improved the availability by 10%, which fully verified the effectiveness of the opportunistic maintenance strategy. Full article

(This article belongs to the Special Issue Condition Monitoring of Mechanical Transmission Systems)

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