Planetary bearing defect detection in a commercial helicopter main gearbox with vibration and acoustic emission

Elasha, Faris; Greaves, Matthew; Mba, David

doi:10.1177/1475921717738713

Cited by 59 publications

(31 citation statements)

References 62 publications

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“…In recent years AE has been applied to the problem, since the frequency of interest is higher than that generated by sliding and friction in the bearing. This has been shown to enable AE monitoring to identify the presence of damage significantly earlier than vibration techniques (Elasha et al 2018).…”

Section: Dedicated Aeronautics Applications and Examplesmentioning

confidence: 99%

Acoustic Emission

Aggelis

Sause

Paćko

et al. 2021

Structural Health Monitoring Damage Detection Systems for Aerospace

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Acoustic emission (AE) is one of the most promising methods for structural health monitoring (SHM) of materials and structures. Because of its passive and non-invasive nature, it can be used during the operation of a structure and supply information that cannot be collected in real time through other techniques. It is based on the recording and study of the elastic waves that are excited by irreversible processes, such as crack nucleation and propagation. These signals are sensed by transducers and are transformed into electric waveforms that offer information on the location and the type of the source. This chapter intends to present the basic principles, the equipment, and the recent trends and applications in aeronautics, highlighting the role of AE in modern non-destructive testing and SHM. The literature in the field is vast; therefore, although the included references provide an idea of the basics and the contemporary interest and level of research and practice, they are just a fraction of the total possible list of worthy studies published in the recent years.

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Section: Dedicated Aeronautics Applications and Examplesmentioning

confidence: 99%

Acoustic Emission

Aggelis

Sause

Paćko

et al. 2021

Structural Health Monitoring Damage Detection Systems for Aerospace

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“…2 represents the available sensors that can be used in FDD systems, which is based on detection level [36]. Chacon et al stated that AE detects faults earlier than other sensors [37] and can thus provide early detection when faults are present.…”

Section: Fault Detection and Diagnosis Stagesmentioning

confidence: 99%

Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review

et al. 2019

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In the age of industry 4.0, deep learning has attracted increasing interest for various research applications. In recent years, deep learning models have been extensively implemented in machinery fault detection and diagnosis (FDD) systems. The deep architecture's automated feature learning process offers great potential to solve problems with traditional fault detection and diagnosis (TFDD) systems. TFDD relies on manual feature selection, which requires prior knowledge of the data and is time intensive. However, the high performance of deep learning comes with challenges and costs. This paper presents a review of deep learning challenges related to machinery fault detection and diagnosis systems. The potential for future work on deep learning implementation in FDD systems is briefly discussed. INDEX TERMSDeep learning, fault detection and diagnosis, current challenges, future developments. I. INTRODUCTION Safety and reliability are key factors in industrial operations. Rotating machinery is a vital component in many industries, and it is prone to failure due to harsh working conditions and long operational times [1], [2]. Examples of rotating machinery components including gears [3], pumps [4], bearings [5], shafts [6], blades [7], motors [8] and engines [9]. Failures in rotating machinery should be detected as early as possible to prevent critical damage [10] and sudden halt of machine operation. Failures may cause delays in operations and, consequently, tremendous economic loss [11]. For example, petrochemical industries lose around 20 billion dollars per year due to faults in their machine components [12]. According to a report by Duan et al. maintenance accounts for more than 60% of the total cost of aircraft engine components [13]. In the worst case, a machinery component failure may lead to loss of human life. Elasha et al. discussed a caseThe associate editor coordinating the review of this article and approving it for publication was Kezhi Li.

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“…At the same time, the gearbox is one of the components with a high failure rate in the wind turbine [3]. Once failure occurs, it will take much effort to find the problem and repair it, which consumes a lot of time, manpower and material resources, and causes serious economic losses [4][5][6][7]. Therefore, accurate fault diagnosis of gearbox is of great significance to the long-term development of wind turbines [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Gearbox Fault Diagnosis Based on Two-Class NMF Network Under Variable Working Conditions

Wang

Sun

Liu

2021

J. Electr. Eng. Technol.

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The gearbox is an important part of the wind turbine, and it is also a part prone to failure. Most of the fault diagnosis methods for gearboxes lack the ability to adapt to changing operating conditions. Once the operating conditions change, it is necessary to relearn the object characteristics, otherwise it is prone to misjudgment. Therefore, this paper proposes a method based on a two-class non-negative matrix factorization (NMF) network to realize gearbox fault diagnosis under variable operating conditions without additional data. The method is divided into two parts: model training and fault diagnosis. The former takes the basic operating conditions as the benchmark. It extracts the local static characteristics of the fault samples, and trains them into classifier models with different diagnostic functions to construct a network. The latter uses the static distance obtained from the data input network of the new operating condition as an important indicator for detecting the occurrence of a fault and identifying the type of the fault. The experimental results based on the QPZZ-II rotating machinery vibration test stand show that compared with other methods, the algorithm proposed in this paper has a good application effect on the diagnosis under variable operating conditions.

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Planetary bearing defect detection in a commercial helicopter main gearbox with vibration and acoustic emission

Cited by 59 publications

References 62 publications

Acoustic Emission

Acoustic Emission

Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review

Gearbox Fault Diagnosis Based on Two-Class NMF Network Under Variable Working Conditions

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