Junda Zhu

Plastic Bearing Fault Diagnosis Based on a Two-Step Data Mining Approach

Plastic bearings are widely used in medical applications, food processing industries, and semiconductor industries. However, no research on plastic bearing fault diagnostics using vibration sensors has been reported. In this paper, a two-step data mining-based approach for plastic bearing fault diagnostics using vibration sensors is presented. The two-step approach utilizes envelope analysis and empirical mode decomposition (EMD) to preprocess vibration signals and extract frequency domain and time domain fault features as condition indicators (CIs) for plastic bearing fault diagnosis. In the first step, the frequency domain CIs are used by a statistical classification model to identify bearing outer race faults. In the second step, the time domain CIs extracted using EMD are developed to build a k-nearest neighbor algorithm-based fault classifier to identify other types of bearing faults. Seeded fault tests on plastic bearing outer race, inner race, balls, and cage are conducted on a bearing diagnostic test rig and real vibration signals are collected. The effectiveness of the presented fault diagnostic approach is validated using the plastic bearing seeded fault testing data.

Gearbox Tooth Cut Fault Diagnostics Using Acoustic Emission and Vibration Sensors — A Comparative Study

In recent years, acoustic emission (AE) sensors and AE-based techniques have been developed and tested for gearbox fault diagnosis. In general, AE-based techniques require much higher sampling rates than vibration analysis-based techniques for gearbox fault diagnosis. Therefore, it is questionable whether an AE-based technique would give a better or at least the same performance as the vibration analysis-based techniques using the same sampling rate. To answer the question, this paper presents a comparative study for gearbox tooth damage level diagnostics using AE and vibration measurements, the first known attempt to compare the gearbox fault diagnostic performance of AE- and vibration analysis-based approaches using the same sampling rate. Partial tooth cut faults are seeded in a gearbox test rig and experimentally tested in a laboratory. Results have shown that the AE-based approach has the potential to differentiate gear tooth damage levels in comparison with the vibration-based approach. While vibration signals are easily affected by mechanical resonance, the AE signals show more stable performance.

Data Mining Based Full Ceramic Bearing Fault Diagnostic System Using AE Sensors

Full ceramic bearings are considered the first step toward full ceramic, oil-free engines in the future. No research on full ceramic bearing fault diagnostics using acoustic emission (AE) sensors has been reported. Unlike their steel counterparts, signal processing methods to extract effective AE fault characteristic features and fault diagnostic systems for full ceramic bearings have not been developed. In this paper, a data mining based full ceramic bearing diagnostic system using AE based condition indicators (CIs) is presented. The system utilizes a new signal processing method based on Hilbert Huang transform to extract AE fault features for the computation of CIs. These CIs are used to build a data mining based fault classifier using a k-nearest neighbor algorithm. Seeded fault tests on full ceramic bearing outer race, inner race, balls, and cage are conducted on a bearing diagnostic test rig and AE burst data are collected. The effectiveness of the developed fault diagnostic system is validated using real full ceramic bearing seeded fault test data.

Online condition monitoring and remaining useful life prediction of particle contaminated lubrication oil

To increase wind energy production rate, there is a pressing need to improve the wind turbine availability and reduce the operational and maintenance costs. The safety and reliability of a functioning wind turbine depend largely on the protective properties of the lubrication oil for its drive train subassemblies such as gearbox and means for lubrication oil condition monitoring and degradation detection. The purpose of lubrication oil condition monitoring and degradation detection is to determine whether the oil has deteriorated to such a degree that it no longer fulfills its function. In this paper, particle contamination of lubrication oil and the remaining useful life (RUL) of the particle contaminated lubrication oil are investigated. Physical models are developed to quantify the relationship between particle contamination level and the outputs of commercially available online oil dielectric and viscosity sensors. The effectiveness of the developed models is then validated using laboratory experiments. In particular, the remaining useful life prediction of degraded lubrication oil with viscosity and dielectric constant data using particle filtering is presented. A simulation case study is provided to demonstrate the effectiveness of the developed technique.

Development of a new acoustic emission based fault diagnosis tool for gearbox

Acoustic emission (AE) has been studied as a potential information source for machine fault diagnosis for a long time. However, AE sensors have not yet been applied widely in real applications. Firstly, in comparison with other sensors such as vibration, AE sensors require much higher sampling rate. The characteristic frequency of AE signals generally falls into the range of 100 kHz to several MHz, which requires a sampling system with at least 5MHz sampling rate. Secondly, the storage and computational burden for large volume of AE data is tremendous. Thirdly, AE signal generally contains certain nonstationary behaviors which make traditional frequency analysis ineffective. In this paper, a frequency reduction technique and a modified time synchronous average (TSA) based signal processing method are proposed to identify gear fault using AE signals. Heterodyne technique commonly used in communication is employed to preprocess the AE signals before sampling. By heterodyning, the AE signal frequency is down shifted from several hundred kHz to below 50 kHz. Then a low sampling rate comparable to that of vibration sensors could be applied to sample the AE signals. After that, a modified tachometer less TSA method is adopted to further analyze the AE signal feature. Instead of performing TSA on the raw signals, the time synchronous averaging of the first order harmonic signal is obtained and analyzed. With the presented method, no tachometer or real time phase reference signal is required. The TSA reference signal is directly obtained from AE signals. By examining the smoothness of obtained wave form, a noticeable discontinuity or irregularity could be easily observed for gear fault diagnosis. AE data collected from seeded fault tests on a gearbox are used to validate the proposed method. The analysis results of the tests have shown that the proposed method could reliably and accurately detect the tooth fault.

Development and evaluation of AE based condition indicators for full ceramic bearing fault diagnosis

Full ceramic bearings are considered the first step towards full ceramic, oil free engines in the future. No research on full ceramic bearing fault diagnostics using acoustic emission (AE) sensors has been reported. Unlike their steel counterparts, AE fault characteristic features of full ceramic bearings and effective signal processing methods for extracting these characteristic features have not been established. In this paper, the development and evaluation of AE based condition indicators (CIs) for full ceramic bearing fault detection and diagnosis are presented. The AE based CIs are developed using an empirical mode decomposition (EMD) based AE feature extraction method and evaluated by testing bearings with seeded outer race, inner race, ball, and cage faults on a bearing diagnostic test rig.

Investigation on full ceramic bearing fault diagnostics using vibration and AE sensors

Full ceramic bearings are considered the first step towards full ceramic and oil free engines in the future. Few researches on full ceramic bearing fault diagnostics using both vibration and acoustic emission (AE) sensors have been reported. In this paper, a research investigation on full ceramic bearing fault diagnostics using vibration and AE sensors is reported. The research utilizes empirical mode decomposition (EMD) to pre-process both vibration and AE signals and a novel multidimensional vibration and AE fault feature extraction method is developed to generate the condition indicators (CIs). These CIs are used to build a fault classifier using a k-nearest neighbor (KNN) algorithm. Seeded fault tests on full ceramic bearing outer race, inner race, balls, and cage are conducted on a bearing fault diagnostic test rig and both vibration signals and AE burst type signals are collected. The effectiveness of the vibration and AE based diagnostic techniques is validated using real full ceramic bearing seeded fault test data. A comparison of fault diagnostic performance between vibration and AE sensors is provided.