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Fig ure 2: (a) A typical FFT spectrum of defected gear vibration signal. (b) Zoom view of a typical FFT spectrum of defected gear vibration signal.
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Gears are critical element in a variety of industrial applications such as machine tool and gearboxes. An unexpected failure of the gear may cause signifi cant economic losses. For that reason, fault diagnosis in gears has been the subject of intensive research. Vibration analysis has been used as a predictive maintenance procedure and as a support...
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... If no peak appears at the fundamental fault frequency, but peaks are present at two, three and maybe four times the fundamental fault frequency, then this also represents a strong indication that the indicated fault is valid. Figure 2(a) shows the spectrum of defected gear vibration signal. In this fi gure, the peaks are found at F m and its second multiple frequencies, but there are some other peaks due to modulation effect of the signal. ...Citations
... The article [1] provides a brief overview of modern vibration-based methods used for condition monitoring in gear transmission systems. The authors of the article draw the following conclusions: ...
This study focuses on the development of a deep learning-based approach of gearbox monitoring and fault detection. The project aims to create a solution for early detection of defects in dynamic equipment based on data from vibration sensor by building a binary classifier with convolutional neural network implemented. The gearboxes condition of which is being assessed is stored in three similar computer numerically controlled (CNC) milling machines. Data is collected during 15 milling operations of different duration and with different tool’s speed and feed. Vibration is measured by an accelerometer stored on the body of each gearbox. Convolutional neural network takes vibration spectra as inputs and whether fault is detected makes a prediction of a gearbox condition. To make the whole solution autonomous and be able to embed it into manufacture the project is integrated into a server with an edge-to-cloud architecture. As an end product deep learning fault classifier stored on a server is to detect possible gearbox faults, draw conclusions on condition of dynamic equipment and automate the process of fault detection.
... However, gears are often subjected to high torques and cyclic loads, which eventually lead to component failures due to fatigue. Gearbox health monitoring is conducted using a variety of methods including wear debris monitoring [1,2], thermography [3] and vibration analysis [4][5][6]. It is common to combine more than one method, e.g., wear debris and vibration trending, to provide a better indication of health through a hybrid model [7]. ...
Detecting gear rim fatigue cracks using vibration signal analysis is often a challenging task, which typically requires a series of signal processing steps to detect and enhance fault features. This task becomes even harder in helicopter planetary gearboxes due to the complex interactions between different gear sets and the presence of vibration from sources other than the planetary gear set. In this paper, we propose an effectual processing algorithm to isolate and enhance rim crack features and to trend crack growth in planet gears. The algorithm is based on using cepstrum editing (or liftering) of the hunting-tooth synchronous averaged signals (angular domain) to extract harmonics and sidebands of the planet gears and low-pass filtering and minimum entropy deconvolution (MED) to enhance extracted fault features. The algorithm has been successfully applied to a vibration dataset collected from a planet gear rim crack propagation test undertaken in the Helicopter Transmission Test Facility (HTTF) at DSTG Melbourne. In this test, a seeded notch generated by an electric discharge machine (EDM) was used to initiate a fatigue crack that propagated through the gear rim body over 94 load cycles. The proposed algorithm demonstrated a successful isolation of incipient fault features and provided a reliable trending capability to monitor crack progression. Results of a comparative analysis showed that the proposed algorithm outperformed the traditional signal processing approach.
... However, gears are often subjected to high torques and cyclic loads which eventually lead to component failures due to fatigue. Gearbox health monitoring is conducted using a variety of methods including wear debris monitoring [1,2], thermography [3], and vibration analysis [4][5][6]. It is common to combine more than one method, e.g. ...
In this paper, an effectual processing algorithm to isolate and enhance rim crack features and to trend crack growth in planet gears is proposed. The algorithm is based on using cepstrum editing (or liftering) of the hunting tooth synchronous averaged signals (angular domain) to extract harmonics and sidebands of the planet gears, and low-pass filtering and minimum entropy deconvolution (MED) to enhance extracted fault features. The algorithm has been successfully applied to a vibration dataset collected from a planet gear rim crack propagation test undertaken in the Helicopter
Transmission Test Facility (HTTF) at DSTG Melbourne. In this test, a seeded notch generated by an electric discharge machine (EDM) was used to initiate a fatigue crack that propagated through the gear rim body over 94 load cycles. The proposed algorithm demonstrated a successful isolation of incipient fault features and
provided a reliable trending capability to monitor crack progression. Results of a comparative analysis showed that the proposed algorithm outperformed the traditional signal processing approach.
... The presence of repetitive transients implies the possibility of an incipient mechanical equipment fault (Randall and Antoni, 2011). In recent years, research on the detection of fault-induced repetitive transients has received increasing attention, especially in the field of rolling bearing and gear fault diagnosis (Miao et al., 2022a;Aherwar, 2015). ...
The extraction of fault-induced repetitive transients which possess cyclo-stationarity is the key to the fault diagnosis of rotating machinery, which is of considerable significance for ensuring the safe and reliable operation of machinery equipment. Traditional deconvolution methods mainly aim to recover fault-related impulsive features from the time domain and are prone to give poor fault diagnosis results under heavy interference conditions. To solve this problem, a spectrum sparse deep deconvolution method (SSDD) with a deep neural network structure is proposed in this paper. The proposed method uses an envelope spectrum sparse criterion as the cost function to seek an optimal inverse filter through a deep neural network. Firstly, a special band-averaging strategy is designed to initialize the filters in the input layer of the neural network with a window method to provide a direction for deconvolution. Secondly, envelope spectral kurtosis that can depict the sparse feature in the envelope spectrum domain is taken as the cost function to guide the training of the deep network and lock the fault information. Then, the optimal weights are realized by the eigenvalue algorithm, and the weak sparse features are enhanced and extracted layer by layer. Finally, the most significant fault information is obtained through dimension reduction. The simulated and experimental data analysis results verified that the proposed method is superior to traditional deconvolution methods in fault diagnosis performance and robustness to random impulses and strong background noise.
... Ideally, fault diagnosis should be managed according to the following hierarchy [1][2][3][4]: fault detection, i.e., separation between healthy and damaged statuses; fault classification, i.e., recognition of the fault type and location; and fault severity estimation (FSE), i.e., evaluation the fault propagation stage or fault size. Currently, gear diagnosis techniques have been developed through three approaches: experimental studies [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], dynamic models [21][22][23][24][25][26][27], and statistical inference using data-driven models and artificial intelligence (AI) [28][29][30][31]. ...
... Ideally, fault diagnosis should be managed according to the following hierarchy [1][2][3][4]: fault detection, i.e., separation between healthy and damaged statuses; fault classification, i.e., recognition of the fault type and location; and fault severity estimation (FSE), i.e., evaluation the fault propagation stage or fault size. Currently, gear diagnosis techniques have been developed through three approaches: experimental studies [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], dynamic models [21][22][23][24][25][26][27], and statistical inference using data-driven models and artificial intelligence (AI) [28][29][30][31]. Vibration analysis is a widely common method for predictive maintenance of rotating machinery [1][2]. ...
Fault diagnosis of gears by vibration analysis has undergone significant growth in recent years. The traditional approaches for gear diagnostics in the past were focused mainly on fault detection. Improved understanding of physics of gear interaction, together with significant progress in dynamic modelling and the new era of artificial intelligence, make it possible for researchers to take on more challenging tasks such as fault severity estimation (FSE) and gear prognosis. This study presents a novel, hybrid strategy for combining physics of failure of machine elements with AI, specifically FSE of tooth breakage faults in spur gears, by a unique fusion of dynamic modelling, experimental characterization, feature extraction, and unsupervised machine learning algorithms. The novelty of the proposed strategy is its logic flow, which starts with a fundamental and deep understanding of the physical phenomena in realistic simulations. The extracted features are selected based on physical insights. The training dataset includes healthy measured data and simulations of healthy and faulty gear to compensate the lack of faulty data in real case scenarios. Discrepancies between the simulations and reality are reduced by passing the simulated signal through transfer functions estimated from experiments. The severity of the fault is estimated according to a "traffic-light" classification strategy, in which, for the first time, the fault geometry is employed to set thresholds for health indicators (HIs). This study shows that a wide set of sensitive features should be used for an early detection, while a smaller and more basic set of features is sufficient for fault severity classification. The suggested strategy is demonstrated on data from an actual testbed; and it is shown to detect breakage faults and estimate their severity, both by zero-shot learning (that is, without training on any measured faulty instances). The strategy can be applied to other systems and likely generalized, thus showing the power of hybrid approaches that combine physical insights with data-driven models.
... For local gear faults such as the levels of a gear tooth crack [8], identification is needed to prevent any unanticipated gear failure because of the tooth breakage of gear initiates due to an incipient crack in the gear [9,10]. Severe vibration is one of the key contributing variables that might lead to an investigation into a local defect in a gearbox [11]. This investigation might be necessary because of the severity of the vibration. ...
Gears are the most important parts of a rotary system, and they are used for mechanical power transmission. The health monitoring of such a system is needed to observe its effective and reliable working. An approach that is based on vibration is typically utilized while carrying out fault diagnostics on a gearbox. Using the Fourier–Bessel series expansion (FBSE) as the basis for an empirical wavelet transform (EWT), a novel automated technique has been proposed in this paper, with a combination of these two approaches, i.e., FBSE-EWT. To improve the frequency resolution, the current empirical wavelet transform will be reformed utilizing the FBSE technique. The proposed novel method includes the decomposition of different levels of gear crack vibration signals into narrow-band components (NBCs) or sub-bands. The Kruskal–Wallis test is utilized to choose the features that are statistically significant in order to separate them from the sub-bands. Three classifiers are used for fault classification, i.e., random forest, J48 decision tree classifiers, and multilayer perceptron function classifier. A comparative study has been performed between the existing EWT and the proposed novel methodology. It has been observed that the FBSE-EWT with a random forest classifier shows a better gear fault detection performance compared to the existing EWT.
... Aherwar [24] presents a review of traditional gear fault detection using vibration analysis techniques, including detailed explanations of commonly used techniques and signal features, such as TSA or FFT. Zhang et al. [25] review various vibration signal-based gear fault diagnostics methods of recent years. ...
Gear condition monitoring can prevent unexpected downtimes or sudden failure of machinery. Since gear damage usually results from tooth contact, data for reliable fault detection should be acquired as close as possible to this engagement to reduce other components' disturbances (such as vibrations). One typical gear damage mechanism is pitting. Although the detection of gear pitting using acceleration data is already covered in research, methods with integrated sensors and electronics into the gear (in-situ) are still in their infancy. Most fault detection approaches still rely on external high-performance measurement systems unsuitable for in-situ integration. Thus, this paper proposes an algorithm pipeline for detecting gear pitting using acceleration data suitable for low-power embedded devices, such as Microcontrollers (MCUs). Downsampling provides the minimum required acceleration data sample rate necessary for detection. It is the basis for future work on suitable sensor and hardware selection. Finally, implementing the algorithm pipeline on a PC and a low-power ARM-Cortex M0+ MCU shows its applicability.
... The gearbox is a complex mechanical system composed of gears, rolling bearings, bearing end covers, transmission shafts, boxes, and other parts. Gearbox casing has a good sealing performance and rigidity, and it plays the role of supporting the rotating mechanism and isolating the external environment [40]. In actual production and life, the faults and failures of gearboxes usually occur on important transmission parts such as gears, transmission shafts, and bearings. ...
The vibration signal acquired by a single sensor contains limited information and is easily interfered by noise signals, resulting in the inability to fully express the operating characteristics and state of a gearbox. To address this problem, our study proposes a gearbox fault diagnosis method based on multi-sensor deep spatiotemporal feature representation. This method utilizes two vibration sensors to obtain the vibration information of the gearbox. A fault diagnosis model (PCNN–GRU) combined with a parallel convolutional neural network (PCNN) and gated recurrent unit (GRU) was used to fuse the gearbox vibration information. The parallel convolutional neural network was used to extract the spatial information of the vibration signals collected by different position sensors, and the timing information was mined through the gated recurrent unit. The deep spatiotemporal features that fuse the multi-sensor spatial and temporal information were composed. The collected multi-sensor vibration signals were directly input into the PCNN–GRU model, and an end-to-end intelligent diagnosis of the gearbox faults was realized. Finally, through experimental verification, the accuracy rate of this model can reach up to 99.92%. Compared with other models, this model has a higher diagnostic accuracy and stability.
... For an healthy bearing, this value is close to 3, and increases with the severity of the fault. It describes how observed data are distributed around the mean (30). Kurtosis can identify the major peaks in the data. ...
... Therefore, the crest factor value will increase if there is a peaks in vibration signal. This feature is applied to detect variations in the signal pattern caused by impulsive vibration sources, such as a defect on the outer race of a bearing (30). For the healthy bearing, this value is nearly 3.5. ...
The important part of mechanical equipment is rotating machinery, used mostly in industrial machinery. Rolling element bearings are the utmost dominant part in rotating machinery, so even small defects in these components could result in catastrophic system failure and enormous financial losses. Hence, it is crucial to create consistent and affordable condition monitoring and fault diagnosis systems that estimate severity level and failure modes and to create an appropriate maintenance strategy. The studies reveal that the fault diagnostic system focuses on single fault diagnosis of the shaft-bearing system. However, in real scenarios, the occurrence of a single fault is very unlikely. Thus, multifault diagnosis of the shaft-bearing system is of greater significance. This paper aims at steadily and broadly summarizing the development of the intelligent multifault diagnostic and condition monitoring systems. In addition, there is a rapid development of application of Internet of things, cloud computing and artificial intelligence techniques for fault diagnosis. In this paper, we summarize the study of various fault diagnostic system built on the architecture and application of these cutting-edge technologies for predictive maintenance of mechanical equipment.
... It has an extensive frequency range, up to 20 kHz, and may be used to assess dynamic changes in mechanical parts, including vibration and shock [26]. The frequency range of piezo-electric accelerometers is broad, with different sizes, shapes, sensitivities, and masses [27]. Typically, accelerometers positioned strategically DIAGNOSTYKA, Vol. 24, No. 2 (2023) Dubaish AA, Jaber AA: Fabrication of a test rig for gearbox fault simulation and diagnosis… 5 on the system measure the vibration signal. ...
Gearboxes are one of the most important and widely exposed to different types of faults in machines. Therefore, manufacturers and researchers have made significant efforts to develop different fault detection and diagnostic approaches for gearboxes. However, many research foundations, such as universities, are currently working on developing different gearbox test rigs to understand the failure mechanisms in gearboxes. As a result, in this article, a gearbox testing rig was proposed and fabricated to evaluate gear performance under low-speed working conditions. It describes the primary mechanical apparatus and the measurement tools used during the experimental analysis of a multistage gearbox transmission system. The data-gathering equipment used to acquire the observed vibration data is also discussed. LabVIEW software was used to build a data acquisition platform using an accelerometer and a NI DAQ device. Then different vibration tests were conducted under different operating conditions, when the gearbox was healthy and then faulty, on this test rig, and the gathered vibration data were analyzed based on time domain signal analysis. The preliminary results are promising and open the horizon for simulating different gearbox test scenarios.
