Pitting damage levels estimation for planetary gear sets based on model simulation and grey relational analysis

The planetary gearbox is a critical mechanism in helicopter transmission systems. Tooth failures in planetary gear sets will cause great risk to helicopter operations. A gear pitting damage level estimation methodology has been devised in this paper by integrating a physical model for simulation signal generation, a three-step statistic algorithm for feature selection and damage level estimation for grey relational analysis. The proposed method was calibrated firstly with fault seeded test data and then validated with the data of other tests from a planetary gear set. The estimation results of test data coincide with the actual test records, showing the effectiveness and accuracy of the method in providing a novel way to model based methods and feature selection and weighting methods for more accurate health monitoring and condition prediction

A hybrid fault diagnosis method for mechanic-electronic-hydraulic control system based on simulated knowledge from virtual prototyping

For the fault diagnosis of mechanic-electronic-hydraulic control system (MEHCS), the main barrier that restricts the application of knowledge-based methods is the lack of historical fault data. Aiming at this problem, this paper proposed a hybrid fault diagnosis method based on simulated knowledge from virtual prototyping. As a special form of mathematical model, virtual prototyping of MEHCS under faulty and nominal condition was established, validated, fault-injected and simulated to obtain simulation data. Fault features of different fault types were extracted, which were then trained by three pattern recognition methods to build the knowledge database for diagnosis. Threshold test and ensemble classifier constituted by the three pattern recognition methods were employed respectively to realize fault detection and isolation. To verify the proposed methodology, a case study of vessel steering system was presented. Fault types of stuck rudder and steady state error were studied. Probabilistic neural network (PNN), naive Bayes (NB), and k-nearest neighbor (kNN) were employed to constitute ensemble classifier based on majority voting. The diagnosis results showed that the accuracy of fault detection and isolation of both fault types were highly acceptable. The ensemble classifier performed better on comprehensiveness and smoothness than any individual pattern recognition method for the overall diagnosis. The proposed method might be an available choice for the fault diagnosis of MEHCS, especially for large-scale and complicated cases

Detection and Diagnosis of Motor Stator Faults using Electric Signals from Variable Speed Drives

Motor current signature analysis has been investigated widely for diagnosing faults of induction motors. However, most of these studies are based on open loop drives. This paper examines the performance of diagnosing motor stator faults under both open and closed loop operation modes. It examines the effectiveness of conventional diagnosis features in both motor current and voltage signals using spectrum analysis. Evaluation results show that the stator fault causes an increase in the sideband amplitude of motor current signature only when the motor is under the open loop control. However, the increase in sidebands can be observed in both the current and voltage signals under the sensorless control mode, showing that it is more promising in diagnosing the stator faults under the sensorless control operation

A bearing fault detection method with low-dimensional compressed measurements of vibration signal

The traditional bearing fault detection method is achieved often by sampling the bearing vibration data under the Shannon sampling theorem. Then the information of the bearing state can be extracted from the vibration data, which is used in fault detection. A long-term and continuous monitoring needs to sample and store large amounts of raw vibration signals, which will burden the data storage and transmission greatly. For this problem, a new bearing fault detection method based on compressed sensing is presented, which just needs to sample and store a small amount of compressed observation data and uses these data directly to achieve the fault detection. Firstly, an over-complete dictionary is trained, on which the vibration signals corresponded to normal state can be decomposed sparsely. Then, the bearing fault detection can be achieved based on the difference of the sparse representation errors between the compressed signals in normal state and fault state on this dictionary. The fault detection results of the proposed method with different parameters are analyzed. The effectiveness of the method is validated by the experimental tests

A hybrid fault diagnosis method for mechanic-electronic-hydraulic control system based on simulated knowledge from virtual prototyping

For the fault diagnosis of mechanic-electronic-hydraulic control system (MEHCS), the main barrier that restricts the application of knowledge-based methods is the lack of historical fault data. Aiming at this problem, this paper proposed a hybrid fault diagnosis method based on simulated knowledge from virtual prototyping. As a special form of mathematical model, virtual prototyping of MEHCS under faulty and nominal condition was established, validated, fault-injected and simulated to obtain simulation data. Fault features of different fault types were extracted, which were then trained by three pattern recognition methods to build the knowledge database for diagnosis. Threshold test and ensemble classifier constituted by the three pattern recognition methods were employed respectively to realize fault detection and isolation. To verify the proposed methodology, a case study of vessel steering system was presented. Fault types of stuck rudder and steady state error were studied. Probabilistic neural network (PNN), naive Bayes (NB), and k-nearest neighbor (kNN) were employed to constitute ensemble classifier based on majority voting. The diagnosis results showed that the accuracy of fault detection and isolation of both fault types were highly acceptable. The ensemble classifier performed better on comprehensiveness and smoothness than any individual pattern recognition method for the overall diagnosis. The proposed method might be an available choice for the fault diagnosis of MEHCS, especially for large-scale and complicated cases

Remaining useful life prediction of rolling bearings by the particle filter method based on degradation rate tracking

There is no doubt that remaining useful life prediction is important to the health management of modern mechanical equipment. But in most cases, the useful operational information of equipment we can get are limited, one of them is vibration signal. Particle filter is a hybrid prediction method combined with data-driven and model-based two kinds of methods. It can solve prognosis problem with the fitted prediction model only by historical data, and allow the uncertainty management. However, the prediction performance of the method is largely dependent on the prediction model and very sensitive to the initial distribution of the model parameters. These flaws limit the further development of particle filter methods in the prediction. Aiming at the shortcomings of the basic particle filter prediction method, a general prediction framework of particle filter based on degradation rate tracking is proposed in this paper. It turned away from the fitted model, and utilized the statistical rule of degradation rate of historical data to estimate and predict the degradation process of system. The effectiveness of the method proposed is validated with useful life prediction case of rolling bearings

An investigation of the orthogonal outputs from an on-rotor MEMS accelerometer for reciprocating compressor condition monitoring

With rapid development in electronics and microelectromechanical systems (MEMS) technology, it becomes possible and attractive to monitor rotor dynamics by directly installing MEMS accelerometers on rotors. This paper studies the mathematical modelling of the orthogonal outputs from an on-rotor MEMS accelerometer and proposes a method to eliminate the gravitational acceleration projected on the measurement axes. This is achieved by shifting the output in the normal direction by π/2π/2 using a Hilbert transform and then combining it with the output of the tangential direction. With further compensation of the combined signal in the frequency domain, the tangential acceleration of the rotor is reconstructed to a high degree of accuracy. Experimental results show that the crankshaft tangential acceleration of a reciprocating compressor, obtained by the proposed method, can discriminate clearly between different discharge pressures and hence can allow common leakage faults to be detected, located and diagnosed for online condition monitoring purposes

A bearing fault detection method based on compressive measurements of vibration signal

The general method for bearing fault detection is achieved by using bearing vibration signals which sampled in the frame of Shannon sampling theory. So it is necessary to sample and save abundant original vibration data in the process of uninterrupted monitoring, and this will generate masses of original data which would burden the storage and transmission. For this issue, a fault detection method based on compressed sensing theory is proposed in this paper. It only needs to sample and save fewer compressive measurements of bearing vibration signal directly compared to original signal. There is no need to recover the original signal accurately for detecting bearing faults, while it just requires referring to the prior training result and reconstructing the overall energy distribution of the original signal in some transform domain. The availability and effectiveness of the method proposed is validated with bearing vibration signals sampled in practice

A bearing fault detection method with low-dimensional compressed measurements of vibration signal

The traditional bearing fault detection method is achieved often by sampling the bearing vibration data under the Shannon sampling theorem. Then the information of the bearing state can be extracted from the vibration data, which is used in fault detection. A long-term and continuous monitoring needs to sample and store large amounts of raw vibration signals, which will burden the data storage and transmission greatly. For this problem, a new bearing fault detection method based on compressed sensing is presented, which just needs to sample and store a small amount of compressed observation data and uses these data directly to achieve the fault detection. Firstly, an over-complete dictionary is trained, on which the vibration signals corresponded to normal state can be decomposed sparsely. Then, the bearing fault detection can be achieved based on the difference of the sparse representation errors between the compressed signals in normal state and fault state on this dictionary. The fault detection results of the proposed method with different parameters are analyzed. The effectiveness of the method is validated by the experimental tests

A study of two bispectral features from envelope signals for bearing fault diagnosis

: To accurately detect and diagnose bearing faults, bispectral analysis has received more attention recently because of its unique property of noise reduction and nonlinearity extraction. Particularly this study investigates two typical bispectra: conventional bispectrum (CB) and modulation signal bispectrum (MSB) for suppressing noise influences in envelope signals and hence obtaining more accurate diagnostic features. The first component from the diagonal slice of CB results and that of the subdiagonal slices of MSB results are taken as the diagnostic features considering effective inclusion of information and easy of computations. Simulative and experimental studies show that both MSB and CB features result in good diagnostic performances but MSB may outperform CB slightly in that it shows smaller variance in attaining the feature and more sensitive to weak fault signatures. This merit of MSB may be due to that the MSB feature has more diagnostic information as it is the combination of first three harmonics, whereas the CB feature is combined from just the first two harmonics