Spectrum Of Vibration Signal Research Articles

Fault Diagnosis of Water Pump Based on Probabilistic Neural Network

The basic principle of probabilistic neural network (PNN) is introduced, which is used in the fault diagnosis of water pump in this paper. The multiple and fractional frequencies in the fault vibration signal spectrum are taken as the feature vectors, and the samples of the fault are established. The probabilistic neural network is trained based on the symptom diagnosis. The result shows that probabilistic neural network can overcome the local optimization of back propagation neural network (BPNN) and meet the requirements for fast diagnosis and high precision diagnosis during fault diagnosis process, so probabilistic neural network can be used in the real time diagnosis, and the fault diagnosis based on probabilistic neural network is feasible.

Vibration Analysis on Ball End Milling Hardened Mould Assembled with Different Hardness Materials

Analytical and experimental identifications on the characteristics of the acceleration signal in high speed milling (HSM) process using ball end mill were studied. Three parts of harden steel with different hardness were assembled together, as opposed to the single harden steel in general HSM process. Dynamic model of ball end milling was established, tests with new and worn cutter were carried out respectively. Time domain analysis on the acceleration signal showed that the amplitude varies with the hardness through the whole cutting path. Furthermore, frequency domain analysis using fast Fourier transform (FFT) technique shows that the spectrum of the vibration signal using new cutter contain the tool spindle rotation speed as fundamental frequency and its harmonics. On the other hand, the spectrum of vibration signal under tool wear state contain other frequencies in addition to the fundamental frequency and harmonics, and this feature of spectrum can be used to monitor the state of the cutter during HSM.

Fault Diagnosis System of Rotating Machinery Vibration Signal

A high demand has been presented in the measuring and diagnosis of vibration signal of rotating machinery, which can reflect the running state information of rotating machinery equipment. This paper designs a new type of fault diagnosis system of rotating machinery vibration signal, which can measure the vibration acceleration and velocity signals accurately, and analyze the vibration severity and frequency division amplitude spectrum of vibration signal. Experiment showed that our system can diagnose typical mechanical fault.

Looseness Diagnosis of Rotating Machinery Via Vibration Analysis Through Hilbert–Huang Transform Approach

The objective of this research in this paper is to investigate the feasibility of utilizing the Hilbert–Huang transform method for diagnosing the looseness faults of rotating machinery. The complicated vibration signals of rotating machinery are decomposed into finite number of intrinsic mode functions (IMFs) by integrated ensemble empirical mode decomposition technique. Through the significance test, the information-contained IMFs are selected to form the neat time-frequency Hilbert spectra and the corresponding marginal Hilbert spectra. The looseness faults at different components of the rotating machinery can be diagnosed by measuring the similarities among the information-contained marginal Hilbert spectra. The fault indicator index is defined to measure the similarities among the information-contained marginal Hilbert spectra of vibration signals. By combining the statistical concept of Mahalanobis distance and cosine index, the fault indicator indices can render the similarities among the marginal Hilbert spectra to enhanced and distinguishable quantities. A test bed of rotor-bearing system is performed to illustrate the looseness faults at different mechanical components. The effectiveness of the proposed approach is evaluated by measuring the fault indicator indices among the marginal Hilbert spectra of different looseness types. The results show that the proposed diagnosis method is capable of classifying the distinction among the marginal Hilbert spectra distributions and thus identify the type of looseness fault at machinery.

Vibration-Based Fault Diagnosis of Hydraulic Pump of Tractor Steering System by Using Energy Technique

This paper focuses on a problem of vibration-based condition monitoring and fault diagnosis of pumps used in the tractor steering system. The vibration-based machine condition monitoring and fault diagnosis incorporate a number of machinery fault detection and diagnostic techniques. The vibration signal from a piezoelectric transducer was captured for the following conditions: Normal pump (GOOD), Journal-bearing with inner face wear (BIFW), gear with tooth face wear (GTFW), and Journal-bearing with inner face wear plus gear with tooth face wear (G&BW) for three working levels of pump speed (1000, 1500, and 2000 rpm). Then Power Spectral Density (PSD) of vibration spectra was calculated. According to the results, fault diagnosis of hydraulic pump is a difficult task using spectrum of vibration signals alone. Results showed that different faults were showed different PSD versus frequency diagram. Comparison of numerical data produced by calculation the area under PSD v. Frequency diagram show that energy technique is an effective method for fault diagnosis of external gear hydraulic pumps.

A New Order Tracking Method Based on Chirplet Path Pursuit and Its Application to Gearbox Fault Diagnosis

Aiming at the problems of order tracking method, such as extra installation of speed measuring devices, low precision of speed acquisition compute methods, and weak antinoise ability, a new order tracking gearbox fault diagnosis method based on Chirplet path pursuit in instantaneous frequency estimation is proposed. Taking the advantage of the said estimation which is suitable to decompose nonstationary signals with frequency variation curve, the method takes speed signal by estimating the gearbox’s meshing frequency component. With the speed signal, it carries out even-angle resampling on even interval sampling signals, transfer non-stationary signals to angular domain stationary signals, and acquire the order spectrum of vibration signals, so as to judge the fault of gearbox. Simulation and application examples prove that the method is of high precision and antinoise ability, which, based on its own features, can adaptively choose base function, and accurately estimate the speed, whose combination with order tracking method can be effectively used in gearbox fault diagnosis.

Induction machine fault detection using clone selection programming

A clonal selection programming (CSP)-based fault detection system is developed for performing induction machine fault detection and analysis. Four feature vectors are extracted from power spectra of machine vibration signals. The extracted features are inputs of an CSP-based classifier for fault identification and classification. In this paper, the proposed CSP-based machine fault diagnostic system has been intensively tested with unbalanced electrical faults and mechanical faults operating at different rotating speeds. The proposed system is not only able to detect electrical and mechanical faults correctly, but the rules generated is also very simple and compact and is easy for people to understand, This will be proved to be extremely useful for practical industrial applications.

Impact dynamics of vibratory microprobe for microcoordinate measurement

This article presents an impact dynamics analysis of the vibratory probe in a microcoordinate measuring machine. The chaotic behavior of the vibratory probe was identified when the driving frequency was changed. A multimode analysis was carried out to investigate the microprobe in both noncontact and contact states. An orthogonal mode transformation was used to maintain continuities when impact occurred. The analytical results indicated that mode coupling and mode coexistence suppressed subbifurcation and pulled down the impact period under particular driving frequencies. To achieve accurate identification of the microprobe in contact with microstructures, the frequency spectra of vibration signals at different contact states were compared. It was shown that the optimum driving frequency should be in the pull-down region. In this region, the dominant frequencies before and after the contact can be clearly separated, resulting in negligible background vibrations.

Induction Machine Fault Detection Using SOM-Based RBF Neural Networks

A radial-basis-function (RBF) neural-network-based fault detection system is developed for performing induction machine fault detection and analysis. Four feature vectors are extracted from power spectra of machine vibration signals. The extracted features are inputs of an RBF-type neural network for fault identification and classification. The optimal network architecture of the RBF network is determined automatically by our proposed cell-splitting grid algorithm. This facilitates the conventional laborious trial-and-error procedure in establishing an optimal architecture. In this paper, the proposed RBF machine fault diagnostic system has been intensively tested with unbalanced electrical faults and mechanical faults operating at different rotating speeds. The proposed system is not only able to detect electrical and mechanical faults, but the system is also able to estimate the extent of faults.

Determination of air-gap eccentricity in electric motors using coherence analysis

This study addresses the detection of air-gap eccentricity based on hearing damage in 5 hp induction machines. The faulty condition of the bearing is caused by accelerated aging processes in seven aging cycles. For the faulty case, the spectra of vibration signals are computed for initial and final aging processes under 100% load condition. Subsequently, the spectra of the current signals for a single phase are also obtained for the initial and aged cases and cross-power spectral coherences between the vibration and current signals are computed.

Diagnosis of faults in rolling element bearings by using directional spectra of vibration signals

Backward and forward defect frequencies of rolling element bearings are experimentally investigated utilizing the two-sided directional spectra of the complex-valued vibration signals measured from the outer ring of defective bearings. The experimental results show that the directional zoom spectrum is superior to the conventional spectrum in identification of bearing defect frequencies, in particular the inner race defect frequencies.

Use of Directional Spectra for Detection of Engine Cylinder Power Fault

A diagnostic method, which uses the two-sided directional power spectra of complex-valued engine vibration signals, is presented and tested with four-cylinder compression and spark ignition engines for the diagnosis of cylinder power faults. As spectral estimators, the maximum likelihood and FFT methods are compared, and the multi-layer neural network is employed for pattern recognition. Experimental results show that the success rate for identifying the misfired cylinder is much higher with the use of two-sided directional power spectra than conventional one-sided power spectra.

Use of Directional Spectra for Detection of Engine Cylinder Power Fault

A diagnostic method, which uses the two-sided directional power spectra of complex-valued engine vibration signals, is presented and tested with four-cylinder compression and spark ignition engines for the diagnosis of cylinder power faults. As spectral estimators, the maximum likelihood and FFT methods are compared, and the multi-layer neural network is employed for pattern recognition. Experimental results show that the success rate for identifying the misfired cylinder is much higher with the use of two-sided directional power spectra than conventional one-sided power spectra.

Noise and Vibration Measurements of 50 kW Vertical Axis Wind Turbine Gear Box

An analysis of noise and vibration measurements was carried out for the gear box and the power house panels of the 50 kW Vertical Axis Wind Turbine (V.A.W.T.) operating at Christopher Point on Vancouver Island, B.C. The spectra of noise and vibration signals show strong peaks at the gear mesh frequency and its harmonics even under light load conditions. The sound power radiated from the power house panels is significantly higher than that radiated by the gear box casing. The analysis also indicates misalignment of the pinion shaft or eccentricity of the pinion and errors in the gear tooth profiles. 6 references.