Unbalance Detection Research Articles

Unbalance detection of threshing cylinder in non-stationary rotation based on variational mode extraction

The rotating speed of the threshing cylinder was hardly constant in practice due to transmission error and cylinder speed would change with feed quantity to ensure the grain threshing effect. This paper proposed a novel unbalance detection method for non-stationary rotation. Variational mode extraction (VME) was introduced to extract unbalanced vibration signal and the unbalance feature index (UFI) was constructed as a criterion of weighting factor selection. Moreover, the unbalance phase calculation method and phase reference in non-stationary were proposed, which could eliminate the influence of speed change. The advantages of VME and UFI were verified by simulation, and the effectiveness of proposed method was proved by experiment on multi-cylinder test rig and combine harvest. This method could realize the reconstruction of unbalanced vibration signal in time-varying speed with low computational cost, break the strict requirement of constant speed of the existing balancing method.

Design and Experimental Research of a Non-Destructive Detection Device for High-Precision Cylindrical Roller Dynamic Unbalance

Due to their small size and light mass, small precision cylindrical rollers present challenges in dynamic unbalance detection, including difficulties in measurement and the risk of surface damage. This paper proposes a non-destructive detection device for assessing the dynamic unbalance of small precision cylindrical rollers. The device utilizes an air flotation support method combined with resonance amplification to indirectly measure the dynamic unbalance. A dynamic model of the air flotation tooling-cylindrical roller vibration system was developed to explore the relationship between the vibration parameters of the air flotation tooling and the dynamic unbalance of the cylindrical roller. Modal analysis and harmonic response analysis were performed, revealing that the amplitude of the vibration system at resonance could be detected using the sensor. Additionally, modal testing was conducted to determine the natural frequency of the system. A non-destructive detection platform was constructed for testing the dynamic unbalance of cylindrical rollers. Microscopic observation of the roller surface before and after testing confirmed that the device successfully performs non-destructive detection of dynamic unbalance.

Sensorless Detection of Mechanical Unbalance in Servodrive with Elastic Coupling

The article focusses on detecting the unbalance of a mechanical component in the electric drive system of a two-mass servomechanism with a permanent magnet synchronous motor (PMSM), which is connected to the load via a long, flexible shaft. In the example analysed, the degree of unbalance was determined using the reference current signal from the speed controller of the field-orientated control (FOC) system. The authors presented a two-mass model with an unbalanced mechanical system. The short-time Fourier transform (STFT) transform was used to analyse the symptoms of unbalance, and an artificial neural network multi-layer perceptron (MLP) was used for system state inference. The effectiveness of the presented analysis, based on the reference current signal from the sensor embedded in the control system, was experimentally confirmed.

Threshing cylinder unbalance detection using a signal extraction method based on parameter-adaptive variational mode decomposition

The threshing cylinder will wear and deform during the threshing process, causing dynamic balance problems. The combine harvester has multiple vibration excitation sources and a complex vibration environment, making it challenging to extract weak unbalanced signals from strong background noise. A novel three-step filtering framework is proposed in this paper. A zero phase filter was used as the pre-processing layer to filter out the high frequency components in the original signal and reduce the number of parameter-adaptive variational mode decompositions (PAVMD) needed. The PAVMD was used to decompose the non-stationary vibration signal before Adaptive Neuron Linear (Adaline) function was used to fit sinusoidal signal parameters. A measurement index, termed the correlation amplitude (CA) index, is constructed. The parameterisation of PAVMD was guided by the CA index, and the modal component of the unbalanced fault features were located. The simulation and real cylinder signals proved that the proposed method could effectively extract unbalanced signals under noise interference, and the unbalance was identified accurately by the influence coefficient method. Experiments on a threshing cylinder showed that the amplitude identification error was <24 g in single-sided unbalance identification, and the amplitude identification error was <27 g in double-sided unbalance identification. The proposed method had high robustness and small identification error, especially under short-time working conditions, compared with other similar approaches.

Development and Investigation of Non-Destructive Detection Drive Mechanism for Precision Type Cylindrical Roller Dynamic Unbalance

In the process of dynamic unbalance detection for precision cylindrical rollers, challenges such as difficulty in achieving effective driving and susceptibility to surface damage during driving significantly impact the accuracy of unbalance detection. This hinders the industry’s ability to achieve the non-destructive detection of cylindrical rollers. Therefore, this paper proposes a novel driving method to enable non-destructive driving of precision cylindrical rollers. The structural principles of the driving mechanism are presented, and a mechanical model for the cylindrical roller is established to analyze the force distribution. Subsequently, a mathematical model for the air film bearing the cylindrical roller is developed to study the variation characteristics of the air film’s load-bearing capacity. The optimal air film thickness is determined, and the rationality of the mathematical model is validated through simulation analysis. Finally, an experimental platform for non-destructive driving is constructed to further verify the effectiveness of the proposed method. This research provides a prerequisite for the non-destructive detection of dynamic unbalance in precision cylindrical rollers.

A Tacholess Order Tracking Method Based on the STFTSC Algorithm for Rotor Unbalance Fault Diagnosis Under Variable-Speed Conditions

Abstract Due to the fact that rotors usually operate in a non-stationary mode with changing speeds, the conventional rotor unbalance detection method based on the stationary signal will produce a major “spectrum ambiguity issue” and affect the accuracy of rotor unbalance detection. To this end, a tacholess order tracking method based on the STFTSC algorithm is suggested in this study, where the STFTSC algorithm is developed by combining the short-time Fourier transform and the seam carving algorithm. First, the STFTSC algorithm is utilized to accurately extract the instantaneous frequency (IF) of the rotor and calculate the instantaneous phase under variable-speed conditions. Subsequently, the original signal is resampled in the angular domain to transform the non-stationary time domain signal into a stable angle domain signal, eliminating the effect of the speed variations. Finally, the angular domain signal is transformed into the order domain signal, which uses the discrete Fourier transform and the discrete spectrum correction method to identify the amplitude and phase corresponding to the fundamental frequency component of the signal. The simulation results show that the IF extracted by the STFTSC algorithm has higher extraction accuracy compared with the traditional STFT spectral peak detection method and effectively eliminates the effect of speed fluctuations. A rotor dynamic-balancing experiment shows that the unbalance correction effect based on the STFTSC algorithm is remarkable, with the average unbalance amount decrease rate on the left and right sides being 90.02% and 92.56%, respectively, after a single correction.

Multi-Sensor Fault Diagnosis for Misalignment and Unbalance Detection Using Machine Learning

Rotating machines frequently undergo various faults causing increased maintenance and operation costs. To minimize these costs, effective and intelligent methods are thus required. Different sensor modalities reflecting various faults should continuously be monitored and interpreted to enable these methods. In this work, two sensor modalities, Infrared Thermography (IRT), and vibration are used complementary to form a multi-sensor fault diagnosis system. This system is used to diagnose the three most occurring faults: misalignment, unbalance, and rotor disk eccentricity, as single, dual, and multi-faults in a rotating mechanical system. The high feature processing capabilities of a Deep Convolutional Neural Network (DCNN) and the high predictive capabilities of a Support Vector Machine (SVM) are combined along with the potential dimensionality reduction using Principal Component Analysis (PCA). The results show that the proposed method is robust and signifies its reliability towards the effective diagnosis of considered faults. Further, IRT-based fault diagnosis outperforms the vibration-based classification in all working conditions.

A New Deep Learning Framework for Imbalance Detection of a Rotating Shaft.

Rotor unbalance is the most common cause of vibration in industrial machines. The unbalance can result in efficiency losses and decreased lifetime of bearings and other components, leading to system failure and significant safety risk. Many complex analytical techniques and specific classifiers algorithms have been developed to study rotor imbalance. The classifier algorithms, though simple to use, lack the flexibility to be used efficiently for both low and high numbers of classes. Therefore, a robust multiclass prediction algorithm is needed to efficiently classify the rotor imbalance problem during runtime and avoid the problem's escalation to failure. In this work, a new deep learning (DL) algorithm was developed for detecting the unbalance of a rotating shaft for both binary and multiclass identification. The model was developed by utilizing the depth and efficacy of ResNet and the feature extraction property of Convolutional Neural Network (CNN). The new algorithm outperforms both ResNet and CNN. Accelerometer data collected by a vibration sensor were used to train the algorithm. This time series data were preprocessed to extract important vibration signatures such as Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT). STFT, being a feature-rich characteristic, performs better on our model. Two types of analyses were carried out: (i) balanced vs. unbalanced case detection (two output classes) and (ii) the level of unbalance detection (five output classes). The developed model gave a testing accuracy of 99.23% for the two-class classification and 95.15% for the multilevel unbalance classification. The results suggest that the proposed deep learning framework is robust for both binary and multiclass classification problems. This study provides a robust framework for detecting shaft unbalance of rotating machinery and can serve as a real-time fault detection mechanism in industrial applications.

Unbalance Detection in Induction Motors through Vibration Signals Using Texture Features

The detection of faults in induction motors has been one of the main challenges to the industry in recent years. An effective fault detection method is fundamental to ensure operational security and productivity. Different models for intelligent fault diagnosis have been recently proposed. However, not all of them are accessible for some manufacturing processes because of the black-box approach, the complexity of hyperparameter tuning, high-dimensionality feature vectors, and the need for sophisticated computational resources. In this paper, a method for the detection of an unbalance fault in induction motors based on a low-dimensional feature vector and a low-complexity classification approach is proposed. The feature vector presented in this manuscript is based on texture features, which are a basic tool for image processing and image understanding. Nevertheless, texture features have not been explored as a powerful instrument for induction motor fault analysis. In this approach, texture features are used to analyze a set of vibration signals belonging to two different classes: an unbalanced motor and a healthy motor. Training-validation and testing stages are developed to build and evaluate the performance of the classifier, respectively. The results show higher accuracy and lower training time in comparison with different state-of-the-art approaches.

A Finite Element Model-Based Approach for Rotor Unbalance Detection and Balancing

A Finite Element Model-Based Approach for Rotor Unbalance Detection and Balancing

Vibration signal denoising method based on CEEMDAN and its application in brake disc unbalance detection

The research objective is the application of the modal decomposition methods in dynamic balancing machine detection. This paper analyzes the problem of feature extraction of dynamic unbalanced signals by using complementary EEMD with adaptive noise (CEEMDAN). CEEMDAN was introduced to decompose the original unbalanced signal. The orthogonal coefficient method, wavelet soft threshold method, and autocorrelation function (ACF) was introduced to remove the irrelevant intrinsic mode functions (IMFs). Moreover, we compare and analyze these methods through simulation and experiments. Simulation results demonstrate that the CEEMDAN method greatly reduces the reconstruction error. The feasibility of the proposed method has been verified experimentally, the real machine signal is sampled and the actual imbalance information is extracted for comparison, which proves the superiority of CEEMDAN over EMD and EEMD in practical applications.

Variable Dropout One-Dimensional CNN for Vibration-Based Shaft Unbalance Detection in Industrial Machinery

Variable Dropout One-Dimensional CNN for Vibration-Based Shaft Unbalance Detection in Industrial Machinery

Detection of unbalance and looseness faults in a ventilation turbine using vibration signature analysis

Heavy industry, which generally uses turbomachines, often uses both technologies to perform high-performance vibration monitoring of its production tool. Vibration analysis is one of the means used to monitor the health of rotating machinery in operation. This is part of a policy of forecast maintenance of the industrial production tool. This work is part of the monitoring and diagnosis of rotating machines by vibration analysis taking as an example the X205 circulation fan. Numerical simulation was done to test the capabilities and limitations of a dynamic simulation. The modelling of vibratory phenomena is developed using SolidWorks software to perform a dynamic simulation of the 3D model. The numerical simulations were performed to find the effect of different types of defects, such as defect of unbalance fin fan and bolt joint looseness, on the output of system. The numerical results were confirmed either by experimentally using employing results available in the open literature that existent at factory of cement plant (SCHS) and a good agreement was observed. The proposed and derived model has demonstrated the viability of dynamic simulation approach to rotating machines by vibration analysis and serves as a significant alternative approach to the direct experimentation on the same systems in terms of cost and time.

Online Unbalance Detection and Diagnosis on Large Flexible Rotors by SVR and ANN trained by Dynamic Multibody Simulations

Multiple-stage steam turbine generators, like those found in nuclear power plants, pose special challenges with regards to mechanical unbalance diagnosis. Several factors contribute to a complex vibrational response, which can lead to incorrect assessments if traditional condition monitoring strategies are used without considering the mechanical system as a whole. This, in turn, can lead to prolonged machinery downtime. Several machine learning techniques can be used to integrally correlate mechanical unbalance along the shaft with transducer signals from rotor bearings. Unfortunately, this type of machinery has scarce data regarding faulty behavior. However, a variety of fault conditions can be simulated in order to generate these data using computational models to simulate the dynamic response of individual machines. In the present work, a multibody model of a 640 MW steam turbine flexible rotor is employed to simulate mechanical unbalance in several positions along the shaft. Synchronous components of the resulting vibration signals at each bearing are obtained and utilized as training data for two regression models designed for mechanical unbalance diagnosis. The first approach uses an artificial neural network and the second one utilizes a support vector regression algorithm. In order to test their performance, the stiffness of each bearing in the multibody simulation was altered between 50% and 150% of the training model values, random noise was added to the signal and several dynamic unbalance conditions were simulated. Results show that both approaches can reliably diagnose dynamic rotor unbalance even when there is a typical degree of uncertainty in bearing stiffness values.

Comparison of the Effectiveness of Selected Vibration Signal Analysis Methods in the Rotor Unbalance Detection of PMSM Drive System

Mechanical unbalance is a phenomenon that concerns rotating elements, including rotors in electrical machines. An unbalanced rotor generates vibration, which is transferred to the machine body. The vibration contributes to reducing drive system reliability and, as a consequence, leads to frequent downtime. Therefore, from an economic point of view, monitoring the unbalance of rotating elements is justified. In this paper, the rotor unbalance of a drive system with a permanent magnet synchronous motor (PMSM) was physically modelled using a specially developed shield, with five test masses fixed at the motor shaft. The analysed diagnostic signal was mechanical vibration. Unbalance was detected using selected signal analysis methods, such as frequency-domain methods (classical spectrum analysis FFT and a higher-order bispectrum method) and two methods applied in technical diagnostics (order analysis and orbit method). The efficiency of unbalance symptom detection using these four methods was compared for the frequency controlled PMSM. The properties of the analysed diagnostic methods were assessed and compared in terms of their usefulness in rotor unbalance diagnosis, and the basic features characterizing the usefulness of these methods were determined depending on the operating conditions of the drive. This work could have a significant impact on the process of designing diagnostic systems for PMSM drives.

Feature extraction from multi-axis MEMS sensors for unbalance detection in non-stationary rotating conditions

Conventional rotor balancers are driven by electric motors that maintain the machine rotor/shaft assembly at a constant rotational speed throughout the whole unbalance detection process. The present work expands the operating field of balancing machines, providing an algorithm enabling rotor balancing in non-stationary coasting-down regions, i.e., during the deceleration phase of the rotor/shaft assembly. In this context, the action of the electric motor, or of any other actuator, is just needed to bring the rotor/shaft assembly at the initial target rotational speed, reducing the overall energy consumption. Then, the actuator is deactivated and the unbalance is detected while the rotating mass is decelerating freely. The effectiveness of the proposed unbalance detection algorithm is proven over the case study of a human-powered wheel balancer. The proposed strategy allows the effective detection of static unbalanced masses higher than or equal to 20g considering both 15′′ and 16′′ vehicle wheels.

Faults Detection and Classification under Variable Condition Using Intrinsic Time - Scale Decomposition and Neural Network

Misalignment and unbalance are a common fault occurring in the rotor system. A new approach for detecting misalignment and unbalance problems combining the intrinsic time - scale decomposition (ITD), the root mean square (RMS) and perceptron multilayer network (MLP) is proposed in this paper. Vibration signals of normal condition, misalignment horizontal, misalignment vertical and unbalance with different level are collected under different speed. ITD, nonlinear analysis of signals, was applied to decompose the vibration signals into 8 proper rotation components. The RMS values of 8 components are calculated and using as features vector. Last, the perceptron multilayer network was used for fault identification and classification. The proposed approach accurately classified and detection of unbalance and misalignment; the average accuracy achieved is 97.99%.

Detection and assessment scheme of voltage and current unbalance for three phase synchronous generators using dual numerical techniques based on correlation and deviation percentage concepts

A differentiation in three-phase voltages or currents is termed as unbalance. Unbalance can be caused by unbalanced loads and power system faults. This disturbance results in serious problems in system elements, particularly to synchronous generators, due to the deviation of magnitude, frequency or phase shift. The ability to detect unbalance as well as to identify the severity level of unbalance is very important in power grids. This paper describes a protection scheme for voltage and current unbalance in synchronous generators. The scheme is based on dual numerical techniques application utilizing correlation and deviations percentages for voltage and current signals to detect and assess the unbalance situations. Different types of disturbances are simulated to verify the effectiveness of the proposed scheme using ATP and MATLAB programs. The simulation results prove that the scheme is successful for unbalance detection and assessment of synchronous generators. The different faults and operating conditions have no effect on the proposed scheme reliability. Also, the results show that the proposed scheme is able to classify the unbalance into different severity levels. Besides the obtained results reveal this scheme is effective and secure to the acceptable overloading and temporary faults in power systems.

Unbalance detection in rotating machinery based on support vector machine using time and frequency domain vibration features

The automated diagnostics of the unbalance in a rotor system has been presented in this study based on an artificial intelligence technique called support vector machine. In order to develop a support vector machine–based unbalance diagnosis, first the raw vibration signals in time and frequency domain are measured experimentally from healthy and unbalanced rotor installed on machine fault simulator. Then, three critical statistical features, namely, standard deviation, skewness, and kurtosis are extracted from the time and frequency domain vibration signals. Further, the features are used for training and testing of the support vector machine for building the automated diagnostic system for unbalance in a rotating system. The results from the present study show that the unbalance fault diagnosis can be effectively done based on the developed support vector machine–based methodology. The automated diagnosis of unbalance is possible with the time domain as well as frequency domain features. The results are better with time domain features than frequency domain features. In addition, the diagnosis is performed and found to be robust at most of the operating speeds of the rotor; however, the diagnosis should be avoided to attempt using the present methodology at very lower operating speeds.

Analysis of Acoustic Emission Data for Bearings subject to Unbalance

Acoustic Emission (AE) is an effective nondestructive method for investigating the behavior of materials under stress. In recent decades, AE applications in structural health monitoring have been extended to other areas such as rotating machineries and cutting tools. This research investigates the application of acoustic emission data for unbalance analysis and detection in rotary systems. The AE parameter of interest in this study is a discrete variable that covers the significance of count, duration and amplitude of AE signals. A statistical model based on Zero-Inflated Poisson (ZIP) regression is proposed to handle over-dispersion and excess zeros of the counting data. The ZIP model indicates that faulty bearings can generate more transient wave in the AE waveform. Control charts can easily detect the faulty bearing using the parameters of the ZIP model. Categorical data analysis based on generalized linear models (GLM) is also presented. The results demonstrate the significance of the couple unbalance.