Figure 2 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Sensor mounting position: L1 when the accelerometer is placed directly on a bearing seat; and L2 when the accelerometer is placed on a manufactured bracket: (a) The angle of the accelerometer mounted, and (b) The position of the accelerometer mounted.
Source publication
Testing the reliability of the threshing unit is difficult and thus often neglected before the harvesting season, which can result in breakdown maintenance during peak harvesting time in difficult-to-access areas for sensor mounting. In this paper, the vibration analysis of the threshing condition of the combine harvester was performed by introduci...
Contexts in source publication
Context 1
... major parameters of the threshing unit are shown in Table 2. The measuring position of the accelerometer on the bearing housing is shown in Figure 2. For the vibration measurement and data analysis, the Adash A4900 Vibrio M vibration accelerometer with a magnetic base and the Adash A4900 Vibrio M vibration analyzer were both used. ...
Context 2
... PSD response (RPSD) provides the spectral response of a structure subjected to random excitation and the RPSD plot gives the information as to where the average power is distributed as a function of frequency. This gives the RMS value of the selected frequency range over the entire available frequency range as well as information about the peak g acceleration responses that occur at the resonant frequency on the assembly (Figure 20). Therefore, the bracket deformation is comparatively negligible for the PSD corresponding to the test result and random vibration PSD inputs. ...
Citations
... The modal measurement point model of the frame was established in the DHDAS dynamic signal analysis system, as shown in Figure 3(b). The actual measurement point positions on the frame were numbered according to the measurement point model (Bhandari & Jotautienė, 2022). The force hammer and sensors were connected to the data acquisition instrument according to their numbers. ...
In complex environments, beet harvesters vibrate strongly under the influence of multiple sources of excitation. The modal constraints of the harvester's frame were obtained using modal simulation, and the accuracy of the finite element model was verified through SIMO modal testing. Additionally, field experiments were conducted to collect the vibration signals of the harvester under various conditions. Time-domain analysis revealed that the RMS value of the frame's Z-axis acceleration was highest in sugar beet fields and lowest on unpaved roads. There is a correlation between the operation of working components and changes in amplitude. Frequency domain analysis determined that the main vibration frequency of the frame was in the range of 0–75 Hz, and the operating frequency of the engine (35 Hz) and the power input shaft (12.7 Hz) excites the constrained modal of the frame, which may lead to resonance. Integrating the results of the modal response and vibration testing provides a more comprehensive approach to studying the vibration characteristics of agricultural machinery.
sugar beet harvester; frame; constrained modal; various operating conditions; modal response
... With the rapid development of industrial manufacturing and scientific and technological breakthroughs, machinery and equipment in modern manufacturing are more sophisticated and intelligent than ever. As an indispensable component of rotating machinery, rolling bearings are widely used in machine tools, agricultural machinery, automotive components, large motors, and other fields because of their high precision, low frictional resistance, standardized dimensions, etc [1][2][3][4]. Still, their failures can affect the operating status of * Author to whom any correspondence should be addressed. ...
... In equation (2), U ∈ R (n−1)×(n−1) and V ∈ R 2×2 are orthogonal matrices; Λ is a diagonal matrix; and Λ = (diag (σ 1 , σ 2 ) , 0) or its transpose with rank 2. (3) After multilayer decomposition of the signal, the signal features under complex interference can be extracted, and noise reduction is achieved by inverse reconstruction of the extracted signal. Moreover, MRSVD does not need to solve the reconstruction order of practical singular values, and the signal feature extraction under intense noise is more accurate than SVD. ...
As a critical component in mechanical equipment, rolling bearings play a vital role in industrial production. Effective bearing fault diagnosis provides a more reliable guarantee for the safe operation of the industrial output. Traditional data-driven bearing fault diagnosis methods often have problems such as insufficient fault feature extraction and poor model generalization capabilities, resulting in reduced diagnostic accuracy. To solve these problems and significantly improve the diagnosis accuracy, this paper proposes a novel fault diagnosis method based on multi-resolution singular value decomposition (MRSVD), continuous wavelet transform (CWT), improved convolutional neural network (CNN) enhanced by convolutional block attention module, and long short-term memory (LSTM). Through MRSVD, the vibration signal is decomposed layer by layer into multiple denoised signals, thus signal noise can be eliminated to the greatest extent to gain the optimal denoised signals; then through CWT, the optimal denoised signals are converted into two-dimensional time-frequency images so that the local and global characteristic information can be fully captured. Finally, through improved CNN-LSTM, feature extraction is greatly enhanced, resulting in high accuracy of fault diagnosis. Lots of experiments are organized to test the performance, and the experimental results show that the proposed method on various datasets has better diagnosis accuracy and generalization ability under different working conditions than other methods.
... In the threshing process, the straw wrapped around the threshing drum will inevitably lead to incentive imbalance, and the grain bears the threshing load during the threshing process [9]. The unbalanced excitation of the threshing drum not only comes from the winding of straw, but also the state excitation signal of the continuous roller bearing used for threshing in the threshing drum device will cause the central trajectory of the central axis of the threshing drum to change [10]. In order to observe the change in the central trajectory more accurately, vibration changes on threshing cylinders are detected with a monitoring system [11]. ...
As a result of the uneven growth of rice, unbalanced vibration of threshing drum caused by stalk entanglement in combine harvester is more and more severe. In order to reveal the influence of unbalanced excitation on the roller axis locus during rice threshing, the stability of threshing drum was studied. The dynamic signal test and analysis system are used to test the axial trajectory of threshing drum. At the same time, the influence of the unbalanced excitation caused by the axis winding on the axis trajectory is analyzed by the experimental results. Axis locus rules under no-load and threshing conditions are obtained. In order to simulate the axial and radial distribution of unbalanced excitation along the threshing drum, the counterweight was distributed on the threshing drum instead of the entangled stalk. Then, the definite effect of unbalanced excitation on the rotating stability of threshing drum is analyzed. Results show that the amplitude of stem winding along the grain drum is larger in the vertical direction and smaller in the horizontal direction when compared with the unloaded state under 200 g weight. It was found that the amplitude in both horizontal and vertical directions decreased after 400 g and 600 g counterweights were added, respectively, to simulate the radial distribution of stalk winding along the grain barrel. Finally, it can be seen that with the increase in the weight of the counterweight, the characteristics of the trajectory misalignment of the threshing cylinder axis become more and more obvious. This study can provide reference for reducing the unbalanced excitation signal of threshing drum and improving driving comfort.
... In reference [26], a fault diagnosis method utilizing composite scale variable dispersion entropy (CSvDE) and self-optimization variational mode decomposition (SoVMD) is presented, showcasing superiority in balancing datasets but lacking applicability to small sample data. Moving on to reference [27], DDS Adash software is employed for signal processing, utilizing the Demodulation Fast Fourier Transform (FFT) root mean square (RMS) method and the DDS Adash Fault Source Identification Tool (FASIT) technique. However, this method falls short in early fault diagnosis of bearings. ...
In the context of addressing the challenge posed by limited fault samples in agricultural machinery rolling bearings, especially when early fault characteristics are subtle, this study introduces a novel approach. The proposed multi-domain fault diagnosis method, anchored in data augmentation, aims to discern early faults in agricultural machinery rolling bearings, particularly within an imbalanced sample framework. The methodology involves determining early fault signals throughout the life cycle, constructing early fault datasets with varying imbalance rates for different fault types, and subsequently employing the Synthetic Minority Oversampling Technique (SMOTE) to balance the fault data. The study then extracts relative wavelet packet energy and time-domain sensitive features (variance, peak to peak) from the original and generated fault data to form a multi-domain fault feature vector. This vector is utilized for fault state recognition using a Support Vector Machine (SVM). Evaluation metrics such as accuracy, recall, and F1 values assess the recognition effectiveness for each rolling bearing state, with the overall model recognition evaluated based on accuracy. The proposed method is rigorously analyzed and validated using the XJTU-SY rolling bearing accelerated life test dataset. Comparative analysis is conducted with non-data enhanced fault feature vectors, specifically the relative energy of the wavelet packet, both with and without time-domain features. Experimental results underscore the superior performance of multi-domain fault features in providing a comprehensive description of signal information, leading to enhanced classification performance. Furthermore, the study demonstrates improved classification accuracy and recall rates for the balanced dataset compared to the imbalanced dataset. This research significantly contributes to an effective identification method for the early fault diagnosis of small sample rolling bearings in agricultural machinery.
... In terms of multiresolution analysis, FFT (Fast Fourier Transform) [36,37] signal processing has proved to be a valuable technique for UAV fault diagnosis [38]. In this context, FFT is used to analyze vibration signals recorded during operation from various UAV components. ...
In the modern technological advancements, Unmanned Aerial Vehicles (UAVs) have emerged across diverse applications. As UAVs evolve, fault diagnosis witnessed great advancements, with signal processing methodologies taking center stage. This paper presents an assessment of vibration-based signal processing techniques, focusing on Kalman filtering (KF) and Discrete Wavelet Transform (DWT) multiresolution analysis. Experimental evaluation of healthy and faulty states in a quadcopter, using an accelerometer, are presented. The determination of the 1024 Hz sampling frequency is facilitated through finite element analysis of 20 mode shapes. KF exhibits commendable performance, successfully segregating faulty and healthy peaks within an acceptable range. While the six-level multi-decomposition unveils good explanations for fluctuations eluding KF. Ultimately, both KF and DWT showcase high-performance capabilities in fault diagnosis. However, DWT shows superior assessment precision, uncovering intricate details and facilitating a holistic understanding of fault-related characteristics.
... Figure 6a depicts the result of Fast Fourier transform (FFT) on the acceleration data measured at Node 7, confirming that the main vibration mode was at 3.25 Hz. The RMS value of the FFT amplitude was employed to intuitively understand the magnitude of the attenuated energy both before and after attaching the vibration reduction device (Escaler et al., 2006;Valentín et al., 2019;Al-Obaidi, 2020;Bhandari & Jotautienė, 2022). To verify whether the Table 1. ...
Unexpected damages or failures of steel pipes in refineries cause significant disruption to economic activity. While research has been conducted on the prevention of damage to steel pipes, no systematic methods or practical techniques for monitoring of vibrations to estimate the state of pipeline system have been reported. In this study, vibration safety evaluation model consisting of design – evaluation – control steps was developed to measure and control the vibration level during operation of the piping system of an oil refinery. The measurement location was designed by examining the structure of the pipe, and the vibration level measured at each location was compared with the allowable vibration level. Subsequently, two types of vibration reduction measures, namely, dynamic absorbers and viscous dampers, were introduced to reduce the vibration level. The effect of the application of the monitoring system was evaluated by comparing the vibration levels of the steel pipes before and after the application of the dynamic absorbers and viscous dampers. The vibrations of steel pipes in the oil refinery during operation decreased by over 50%. Upon applying the dynamic absorbers and viscous dampers, the responses of the frequency component also exhibited local and global reductions of approximately 50–80%.
... Signals from faulty components exhibit non-stationary behavior. However, if the frequency section of non-stationary signals is computed using the Fourier transform, the results will reflect the frequency composition averaged across the signal period [17,18]. Time-frequency analysis techniques are suitable for non-stationary transformations due to this differentiating feature. ...
As a modern technological trend, unmanned aerial vehicles (UAVs) are extensively employed in various applications. The core purpose of condition monitoring systems, proactive fault diagnosis, is essential in ensuring UAV safety in these applications. In this research, adaptive health monitoring systems perform blade balancing fault diagnosis and classification. There seems to be a bidirectional unpredictability within each, and this paper proposes a hybrid-based transformed discrete wavelet and a multi-hidden-layer deep neural network (DNN) scheme to compensate for it. Wide-scale, high-quality, and comprehensive soft-labeled data are extracted from a selected hovering quad-copter incorporated with an accelerometer sensor via experimental work. A data-driven intelligent diagnostic strategy was investigated. Statistical characteristics of non-stationary six-lev-eled multi-resolution analysis in three axes are acquired. Two important feature selection methods were adopted to minimize computing time and improve classification accuracy when progressed into an artificial intelligence (AI) model for fault diagnosis. The suggested approach offers exceptional potential: the fault detection system identifies and predicts faults accurately as the resulting 91% classification accuracy exceeds current state-of-the-art fault diagnosis strategies. The proposed model demonstrated operational applicability on any multirotor UAV of choice.
