High-frequency Resonance Technique Research Articles

Enhanced adaptive high-frequency resonance technology for bearing fault detection

The traditional high-frequency resonance technology has two disadvantages: (1) The resonance frequency (RF) is obtained by using Fourier spectrum (FS) and the bandwidth is obtained based on experience, and the effect is often poor due to severe noise interference; (2) the separation of compound faults is not considered. After systematic research, a feasible fault detection methodology named enhanced adaptive high-frequency resonance technology is summarized and introduced, which is on the basis of power spectrum (PS) analysis, abbreviated as enhanced adaptive high-frequency resonance technique based on power spectrum (PS-EAHFRT), to realize the single fault detection and compound fault separation of bearing. Firstly, the theoretical values of various bearing fault modes are calculated to establish a database. Then, a series of spectral peaks are obtained by performing PS analysis, and each prominent spectral peak is considered as an alternative RF, that is, an alternative central frequency. Then, the bandwidth calculation formula of the bandpass filter is given, and the optimal bandwidth parameter is calculated by using the signal envelope kurtosis as a sparse measure. Finally, the original signal is denoised by the optimized band-pass filter, and then the filtered signal is demodulated by envelope PS analysis, and compared with the theoretical value in the library, and then the fault category is obtained. Simulated and real-world bearing cases are used to demonstrate the effectiveness and advantages of PS-EAHFRT. It is demonstrated that the PS-EAHFRT can effectively realize the bearing single fault detection and the compound fault separation. Moreover, the lower bound of the frequency interval in which this method can achieve bearing composite fault separation is explored.

Diagnosis of localized defects in floating bush bearings through time-frequency domain analysis

Bearings play a crucial role in the functionality of rotating machinery, and any defects in these components can result in machine failure. Detecting, diagnosing, and prognosing bearing faults are crucial steps in machine failure diagnostics to prevent malfunctions and breakdowns. While various methods exist for fault detection, including acoustic emission analysis, visual inspection, thermography, ultrasonic, motor current analysis, wear-debris analysis, oil analysis, and vibration analysis, the latter stands out as a popular non-destructive method. This paper focuses on time and frequency domain vibration analysis techniques for detecting faults in floating bush bearings. Particularly beneficial for online monitoring, remote and non-human intervention areas, and hazardous locations, the time-frequency domain approach enhances diagnostic capabilities. The vibration data collected during these experiments has been rigorously analyzed using data acquisition system and applied a comprehensive approach that includes evaluating the data in both the time and frequency domains, as well as utilizing advanced signal processing techniques, notably the high-frequency resonance technique. Test results underscore the effectiveness of specific parameters in identifying defects: waveform, form factor, parameter K, and cepstrum excel in pinpointing external defects, while kurtosis, crest factor, and skewness prove adept at identifying internal faults. In the frequency domain, the enveloped spectrum emerges as a robust method for comprehensive defect detection. The vibration data presented in this paper will prove to be an invaluable resource for professionals engaged in the field of vibration measurement and analysis.

An approach to improve high-frequency resonance technique for bearing fault diagnosis

The defect frequency amplitude of the High-Frequency Resonance Technique spectrum is a useful feature for bearing diagnosis. However, it is dependent on the accuracy of the resonance frequency band selection. The vibration data are filtered, with the resonance frequency band being the passband. An alternative approach based on Empirical Mode Decomposition and Instantaneous Energy is proposed to obtain the filtered signal without the need of identifying the resonance frequency band. The objective is to maximize the defect frequency amplitude of the High-Frequency Resonance Technique spectrum. The proposed approach is compared with various optimization techniques and the frequently used Fast Kurtogram approach. These approaches are compared for the simulated and experimental data (seeded and natural defects). The comparison reveals that the proposed approach performs superior to the Fast Kurtogram approach and close to optimization algorithms (the best possible) in a fraction of the time taken by these techniques.

Fault feature extraction based on combination of envelope order tracking and cICA for rolling element bearings

Abstract Vibration from incipient faults of rolling element bearings (REBs) is usually too weak to be observed in a conventional spectrum analysis. The envelope analysis or high-frequency resonance technique is an effective tool for the incipient fault detection of REBs. The newly developed envelope order tracking, an improved version of the envelope analysis, can be well performed even in a varying-speed condition. However, the envelope order tracking can be invalid for multi-impulsive sources. To address this issue, a scheme for the weak feature extraction of faulty REBs has been proposed in this paper by combining the envelope order tracking and the constrained independent component analysis (cICA). In the proposed scheme, the envelope order tracking approach is utilized to obtain the envelopes of sensor observed mixtures at different positions. Then, the envelopes are turned from the time domain into the angle domain by the constant-angle increments resampling scheme in the computed order tracking (COT). Subsequently, the cICA method is employed to extract the interesting envelope independent components (ICs) by a reference signal, which is constructed according to the prior-known feature frequency of the bearing. As a result, the faults related features can be clearly exposed in the spectra of the obtained interesting envelope ICs. Simulations and experimental results support the proposed method positively.

Bearing fault identification by higher order energy operator fusion: A non-resonance based approach

We report a non-resonance based approach to bearing fault detection. This is achieved by a higher order energy operator fusion (HOEO_F) method. In this method, multiple higher order energy operators are fused to form a single simple transform to process the bearing signal obscured by noise and vibration interferences. The fusion is guided by entropy minimization. Unlike the popular high frequency resonance technique, this method does not require the information of resonance excited by the bearing fault. The effects of the HOEO_F method on signal-to-noise ratio (SNR) and signal-to-interference ratio (SIR) are illustrated in this paper. The performance of the proposed method in handling noise and interferences has been examined using both simulated and experimental data. The results indicate that the HOEO_F method outperforms both the envelope method and the original energy operator method.

Application of the wavelets multiresolution analysis and the high-frequency resonance technique for gears and bearings faults diagnosis

Defects diagnosis and condition surveillance of production and manufacturing rotating machinery in a plant is very important for guaranteeing production efficiency and plant safety. Condition surveillance for gear and bearing defects diagnosis for all rotating machines is a serious job because they cause accidents and consequently great production losses. For gear and bearing faults, and early detection especially in the gearboxes, researchers in the conditional maintenance and vibratory analysis used different methods and techniques in signal processing, among those and in full rise, demodulation by wavelets multiresolution analysis (WMRA) and high-frequency resonance technique (HFRT), based on the Hilbert transform, which allows filtering and the demodulation at the same time. In this paper, we propose to make a precise diagnosis for gears and bearings combined faults detection and identification in a laboratory test rig which simulate a rotating machine like in the manufacturing processes using WMRA and HFRT techniques. First of all, we applied WMRA method on simulated signals of gear or bearing defects or the combination of them, then we applied it on real signals measured on a test rig of the LMS laboratory in the University of Guelma.

Application of HFRT Methods to Diagnose the Technical Condition of High-Speed Marine Diesel Engines

The paper presents problems related to testing of the technical condition of high-speed marine diesel engines that are not equipped with indicated valves, as it is in the case of larger medium-and low-speed marine internal combustion engines. In this case, in assessment of technical condition of engine fuel injection system and valve gear system a vibration signals (in time / angle domain) analysis modified method called HFRT (High Frequency Resonance Technique) can be used. This method indirectly helps also to evaluate the fuel combustion process in the engine cylinders. The paper presents the theoretical basis of a modified HFRT method, physical implementation of the marine diesel engine system’s analyzer used for marine engines testing built at the Institute of Construction and Operation of Ships at Polish Naval Academy (PNA) in Gdynia. The paper also includes a description of the vibration signal processing methodology and examples of measurements made in the ships conditions for a few selected types of engines.

Automatic Condition Monitoring of Industrial Rolling-Element Bearings Using Motor’s Vibration and Current Analysis

An automatic condition monitoring for a class of industrial rolling-element bearings is developed based on the vibration as well as stator current analysis. The considered fault scenarios include a single-point defect, multiple-point defects, and a type of distributed defect. Motivated by the potential commercialization, the developed system is promoted mainly using off-the-shelf techniques, that is, the high-frequency resonance technique with envelope detection and the average of short-time Fourier transform. In order to test the flexibility and robustness, the monitoring performance is extensively studied under diverse operating conditions: different sensor locations, motor speeds, loading conditions, and data samples from different time segments. The experimental results showed the powerful capability of vibration analysis in the bearing point defect fault diagnosis. The current analysis also showed a moderate capability in diagnosis of point defect faults depending on the type of fault, severity of the fault, and the operational condition. The temporal feature indicated a feasibility to detect generalized roughness fault. The practical issues, such as deviations of predicted characteristic frequencies, sideband effects, time-average of spectra, and selection of fault index and thresholds, are also discussed. The experimental work shows a huge potential to use some simple methods for successful diagnosis of industrial bearing systems.

Digital Systems Design Based on DSP Algorithms in FPGA for Fault Identification in Rotary Machines

This paper deals with a top down design methodology to DSP algorithms based on FPGA. Additionally, DSP algorithms, such as FIR filters, fast Fourier transform (FFT), and the high-frequency resonance technique (HFRT), are implemented in the hardware embedded in the FPGA using a MATLAB/SIMULINK model. The principle idea of this approach is the advantage of a MATLAB/SIMULINK model created to be generic, flexible and can be easily changed at the end user. To validate our approach, an HFRT algorithm to identification the fault in rolling bearings is implemented in FPGA.

A Review of Dynamic Modeling and Fault Identifications Methods for Rolling Element Bearing

Abstract The rolling elements bearings are widely used in industrial and domestic machines. The existence of even tiny defects on the mating surfaces of the bearing components can lead to failure through passage of time. Their failure leads to economical and personal losses. The vibration monitoring technique is mostly used in the industries for health monitoring of bearings. Significant studies are available in open literature for vibration analysis of healthy and defective rolling elements bearings. Various researchers have studied the vibrations generated by bearings through theoretical model and experimentations. The researchers have developed the dynamic model of shaft bearing systems for the theoretical studies. This paper reviews different dynamic models for rolling bearing in presence and absence of local and distributed defects. Moreover, the techniques used for the improvement of fault detection have also been summarized. The signal processing techniques like wavelet transform, high frequency resonance technique (HFRT), envelope analysis and cyclic autocorrelation have improved the fault detection.

Maximization of the Signal Impulsiveness Combining Envelope Technique with Minimum Entropy Deconvolution

The application of the HFRT (High-Frequency Resonance Technique), a demodulation based technique, is a technique for evaluation the condition of bearings and other components in rotating machinery. Another technique MED (Minimum Entropy Deconvolution) has been the subject of recent developments for application in condition monitoring of gear trains and roller bearings. This article demonstrates the effectiveness of the combined application of the MED technique with HFRT in order to enhance the capacity of HFRT to identify the characteristic fault frequencies of damaged bearings by increasing the signal impulsivity. All tests were done using data collected from an experimental test bench in laboratory. The Kurtosis value is used as an indicator of effectiveness of the combined technique and the results shown an increase of five times the original kurtosis value with the application of MED filter together with the HFRT.

Time-Frequency Analysis Combining Wavelets and the Envelope Technique for Fault Identification in Rotary Machines

Nowadays the method based on demodulation by envelope finds wide application in industry as a technique for evaluation of bearings and other components in rotating machinery. In recent years the application of Wavelets for fault diagnosis in machinery has also obtained good development. This article demonstrates the effectiveness of the combined application of Wavelets and envelope technique (also known as HFRT High-Frequency Resonance Technique) to remove background noise from signals collected from defect bearings and identification of the characteristic frequencies of defects. A comparison of the results obtained with the isolated application of only one method against the combined technique is performed showing the increased capacity in detection of faults in rolling bearings.

Development of Monitoring and Diagnosis System for Linear Motion Unit

In the present work, investigations by high frequency resonance technique for diagnosis of defect frequencies of linear motion unit are reported. Raw vibration signature of the moving parts at different speeds of operation has been demodulated. Envelope detected spectrum is analyzed to evaluate various defect frequencies and their energy levels. Experimentally evaluated frequencies are compared with theoretically determined defect frequencies. These frequency values and their energy levels are used to monitor intrinsic condition of linear motion unit as well as to establish severity of existing/developed defects on the LM guide and inside the LM block. Relative comparisons of linear motion units of the same type are made at various operating speeds under identical conditions of operation on the basis of identified defect frequencies and severity of defects.

Bearing Multiple Defects Detection Based on Envelope Detector Time Constant

Rolling element bearing damage detection is one of the foremost concerns in rotating machinery. The difficulties in bearing defect diagnosis when the bearing has multiple defects increase, since unexpected changes occur in the amplitude of the bearing defect frequencies. In addition, the tendency toward condition-based maintenance (CBM) requires a better understanding of the fault progression due to the fact that multiple defects is one kind of fault development. In this paper, in order to detect multiple defects on one component of the bearing, a new method based on the high frequency resonance technique (HFRT) is introduced. The time constant in the envelope detector is used to find the pattern of the amplitude of defect frequency harmonics (ADFH) in the frequency domain. This method is based on a comparison of the ADFH with a curve, which is obtained from vibration modeling of the bearing. Two criteria are given for the diagnosis of multiple defects. The method is investigated with a simulation and a real experiment. Single and multiple defects are created on the outer race of the ball bearing at different angles. Additionally, the ADFH in the multiple faults experiments are calculated with the proposed mathematical modeling in order to check the accuracy of the model. The experimental results confirm the ability of the proposed method to diagnose multiple defects.

Research on on-line automatic diagnostic technology for scratch defect of rolling element bearings

An on-line automatic diagnostic technology is presented in this paper, based on the simplified vibration model of early scratch defects of rolling element bearings and the analysis of vibration characteristics of early scratch defects. According to characteristics of periodic high frequency natural vibration excited by early scratch defects of the bearing, high frequency resonance technique (HFRT) is adopted for on-line diagnosing in the paper. An algorithm on peak detection of the vibrational energy is used to catch the main frequency of high frequency resonance automatically, denoising technology to improve the signal-to-noise ratio, and automatic identification technology to evaluate defect site of the tested bearing. An on-line automatic diagnostic system for early scratch defects of rolling element bearings, developed based on the above principles, can effectively identify early scratches on outer race, inner race and ball.

Energy Operator Demodulation Method and Its Application in Gears Fault Diagnosis

Base on the analysis of the localization of the demodulation methods, a new optimizing arithmetic model, the energy operator optimizing arithmetic, is put forward first. Through theoretical analysis and engineering application, this new arithmetic is compared with the classic arithmetic, Hilbert demodulating method, from the precision, sensitivity, anti-jamming, complexion and some other fields by experiments. At last, this dissertation validates that the high-frequency resonance technique based on energy operator optimizing arithmetic which combine with LabView is availability with gear fault diagnosis that increase the frequency resolution of demodulation spectrum.

An envelope analysis based on the resonance modes of the mechanical system for the bearing defect diagnosis

In this paper, an envelope estimation algorithm based on the resonance modes of the mechanical system is proposed and the exponential decay frequency of the envelope signal could be further estimated to be a quantified index for the bearing defect diagnosis. According to the vibration spectrum of the bearing system, the resonance frequencies in the range of its corresponding resonance modes could be initially designated and further revised. Under the assumption of stepwise functions for the envelope signals, the vibration signal could be decomposed into the sinusoidal function bases with fundamental frequencies at the corresponding resonance frequencies. Thus, the envelope signals could be retrieved by estimating the stepwise functions. In addition, the reconstructed signal with noise rejection could be derived from the envelope signals. According to the envelope signals, the exponential decay frequency is estimated to be the diagnosis index of the bearing running condition. In the simulated and experimental studies, it is found that the envelope estimation algorithm could be effectively applied in the signal processing for the bearing vibrations. In addition, the envelope spectra also show good consistency between the proposed method and the high-frequency resonance technique. Finally, it is shown that the exponential decay frequency could successfully be the quantified index for the bearing defect diagnosis.

Parameter-free bearing fault detection based on maximum likelihood estimation and differentiation

Bearing faults can lead to malfunction and ultimately complete stall of many machines. The conventional high-frequency resonance (HFR) method has been commonly used for bearing fault detection. However, it is often very difficult to obtain and calibrate bandpass filter parameters, i.e. the center frequency and bandwidth, the key to the success of the HFR method. This inevitably undermines the usefulness of the conventional HFR technique. To avoid such difficulties, we propose parameter-free, versatile yet straightforward techniques to detect bearing faults. We focus on two types of measured signals frequently encountered in practice: (1) a mixture of impulsive faulty bearing vibrations and intrinsic background noise and (2) impulsive faulty bearing vibrations blended with intrinsic background noise and vibration interferences. To design a proper signal processing technique for each case, we analyze the effects of intrinsic background noise and vibration interferences on amplitude demodulation. For the first case, a maximum likelihood-based fault detection method is proposed to accommodate the Rician distribution of the amplitude-demodulated signal mixture. For the second case, we first illustrate that the high-amplitude low-frequency vibration interferences can make the amplitude demodulation ineffective. Then we propose a differentiation method to enhance the fault detectability. It is shown that the iterative application of a differentiation step can boost the relative strength of the impulsive faulty bearing signal component with respect to the vibration interferences. This preserves the effectiveness of amplitude demodulation and hence leads to more accurate fault detection. The proposed approaches are evaluated on simulated signals and experimental data acquired from faulty bearings.

3D spectral analysis for vibration signals by wavelet-based demodulation

In this paper, a wavelet-based demodulating function is proposed to apply in 3D spectral analysis for vibration signals. In the function, there are three parameters required to assign for adjustment and designation of the filtering passband, which are the low cut-off frequency, the high cut-off frequency and the dilation. Accordingly, it would be convenient to apply in the high-frequency resonance technique. In addition, by sweeping the filtering passband from a low-frequency band to a high-frequency band, a 3D spectrum could be constructed to describe how energy distribution between the instantaneous frequency and the filtering passband for a vibration signal. The 3D spectrum would be helpful to give a clear view of both characteristic frequencies and system resonances, and possesses the advantage of minimising the interventions by the end-user.

APPLICATION OF SYNCHRONOUS AVERAGING TO VIBRATION MONITORING OF ROLLING ELEMENT BEARINGS

In this paper, the high frequency resonance technique, used for many years in the early detection of failure in rolling element bearings by vibration analysis, is combined with the technique of synchronous averaging, which has long been applied to vibration analysis of gears for the early detection of failure. It is shown that by synchronising the averaging of the envelope signal with a trigger signal taken from the rotation of the shaft, a representation is obtained for a bearing under radial load of the influence of the load distribution and the effects of the transfer path to the vibration transducer. By synchronising with the rotation speed of the shaft relative to the cage, an estimate is obtained of the distribution of the damage on the inner race of the bearing, and of the variations between rolling elements. The techniques are demonstrated by experiments on a laboratory test rig with a rolling bearing under radial load and multiple simulated spalls on the inner race.