James Gu

Gearbox is a key part in machinery, in which gear, shaft and bearing operate together to transmit motion and power. The wide usage and high failure rate of gearbox make it attract much attention on its health monitoring and fault diagnosis. Dynamic modeling can study the mechanism under different faults and provide theoretical foundation for fault detection. However, current commonly used gear dynamic model usually neglects the influence of bearing and shaft, resulting in incomplete understanding on gearbox fault diagnosis especially under the effect of local defects on gear and shaft. To address this problem, an improved gear-shaft-bearing-housing dynamic model is proposed to reveal the vibration mechanism and responses considering shaft whirling and gear local defects. Firstly, an eighteen degree-of-freedom gearbox dynamic model is proposed, taking into account the interaction among gear, bearing and shaft. Secondly, the dynamic model is iteratively solved. Then, vibration responses are expounded and analyzed considering gear spalling and shaft crack. Numerical results show that the gear mesh frequency and its harmonics have higher amplitude through the spectrum. Vibration RMS and the shaft rotating frequency increase with the spalling size and shaft crack angle in general. An experiment is designed to verify the rationality of the proposed gearbox model. Lastly, comprehensive analysis under different spalling size and shaft crack angle are analyzed. Results show that when spalling size and crack angle is lager, RMS and the amplitude of shaft rotating frequency will not increase linearly. The dynamic model can accurately simulate the vibration of gear transmission system, which is helpful to gearbox fault diagnosis.

The vibration signal of a faulty rolling bearing exhibits typical non-stationarity - often in the form of cyclostationarity. The spectrum tools often used to characterize cyclostationarity mainly include envelope spectrum, squared envelope spectrum and log-envelope spectrum. In this paper, new detection methods of cyclostationarity are developed for obtaining a larger family of enve-lope analysis and their effectiveness in rolling bearing fault diagnosis is evaluated rigorously. Firstly, based on the simplified Box-Cox transformation, the generalized envelope signals are constructed from the analytic signal for demodulation purposes, and then a spectrum family named generalized envelope spectra (GESs) is proposed to reveal cyclostationarity. Especially, GESs with different transformation parameters exhibit different performance advantages against the random impulse noise and Gaussian background noise which are commonly present in rolling bearing vibration signals. Subsequently, a novel spectrum tool that combines the performance advantages of different GESs, called product envelope spectrum (PES), is developed to strengthen the capability to detect cyclostationarity. Finally, an enhanced envelope analysis named Product Envelope Spectral Optimization-gram (PESOgram) is proposed to improve the accuracy and robustness of PES for rolling bearing fault diagnosis in the presence of different fault-unrelated interference noises. The performance of the PESOgram method is validated on numerically generated signal and experimental signals collected from two railway axle bearing test rigs and compared with several state-of-the-art envelope analysis methods. The results demonstrate the effectiveness of the proposed method for fault diagnosis of rolling bearings and its advantages over other state-of-the-art methods.

The classic Gini index (GI) is generalized recently by using nonlinear weight sequences as sparsity measures for sparse quantification and machine condition monitoring. The generalized GIs with different weight parameters are more robust to random transients. However, they show insufficient performance in discriminating repetitive transients under noise contamination. To overcome this shortage, this paper proposes a two-parameter generalization method to tune not only the weight parameter but also the norm order, allowing for a full generalization of the classic GI to quantify transient features and leading to new statistical indicators which are named fully generalized GIs (FGGIs). Mathematical derivations show that FGGIs satisfy at least four of the six typical attributes of sparsity measures and that those with weight parameter equal to one satisfy at least five sparse attributes, proving that they are a new family of sparsity measures. Numerical simulations demonstrate that FGGIs can monotonically evaluate the sparseness of the signals and that the FGGIs with appropriate parameters exhibit improved performance in resisting random transient interferences and discriminating noise-contaminated repetitive transients compared to traditional sparsity measures. The performance of FGGIs in the condition monitoring of rolling element bearings is validated using two different run-to-failure experiment datasets, including a gradual failure and a sudden failure. The results show that increasing the norm order can improve the capability of FGGIs to characterize transient fault features, allowing more accurate trending of bearing health conditions, and therefore achieving better condition monitoring performance than the traditional sparsity measures.

Bearings are key components in rotating machinery, which is widely used in many fields, such as CNC machines, wind turbines and induction machines. The increasingly harsh operation environment can lead to wear and tear on raceways and reduce the precision and reliability of bearing or even machinery. Lubrication could relieve the wear to some degree, which is benefit to prolong the bearing’s life. Thus, investigation on the vibration responses under the influence of oil film is of great significance. However, for mechanism analysis, how to include the oil film into the bearing dynamic model affects the result and efficiency of solution. To address this problem, this study proposed a fast algorithm through load distribution and interpolation when calculating oil film stiffness and thickness during the solution of bearing vibration model. Analysis of oil film on vibration is carried out and a bearing test rig is designed to verify the proposed model. Numerical simulation result shows that rotational speed and load have vital effect on oil film and vibration, and oil film can play a role of shock absorption. The experimental result is consistent with the simulation, which shows that the proposed model has a better performance on modeling bearing vibration and the method of considering oil film is reasonable.

Gearbox condition monitoring (CM) plays a significant role in ensuring the operational reliability and efficiency of a wide range of critical industrial systems such as wind turbines and helicopters. Accurate and timely diagnosis of gear faults will improve the maintenance of gearboxes operating under sub-optimal conditions, avoid excessive energy consumption and prevent avoidable damages to systems. This study focuses on developing CM for a multi-stage helical gearbox using airborne sound. Based on signal phase alignments, Modulation Signal Bispectrum (MSB) analysis allows random noise and interrupting events in sound signals to be suppressed greatly and obtains nonlinear modulation features in association with gear dynamics. MSB coherence is evaluated for selecting the reliable bi-spectral peaks for indication of gear deterioration. A run-to-failure test of two industrial gearboxes was tested under various loading conditions. Two omnidirectional microphones were fixed near the gearboxes to sense acoustic information during operation. It has been shown that compared against vibration based CM, acoustics can perceive the responses of vibration in a larger areas and contains more comprehensive and stable information related to gear dynamics variation due to wear. Also, the MSB magnitude peaks at the first three harmonic components of gear mesh and rotation components are demonstrated to be sufficient in characterizing the gradual deterioration of gear transmission. Consequently, the combining of MSB peaks with baseline normalization yields more accurate monitoring trends and diagnostics, allowing the gradual deterioration process and gear wear location to be represented more consistently.

•The nonstationarity of vibration signals from rolling bearings can be tuned by envelope to be more stationary.•EAAE denoise is based the ensemble average of phase aligned autocorrelation signals.•EAAE is able to reveal the weak bearing fault signatures under SNR of-30 dB. The rolling element bearings are extensively applied in rotating machines, and they are the most susceptible components in rotating machines. Early fault detection of bearings is to prevent machines from such typical failures and subsequent consequences. In this paper a detector based on Ensemble Average of Autocorrelated Envelopes (EAAE) is proposed to identify the early occurrence faults in rolling element bearings, of which the fault induced vibration signals are inevitably contaminated or masked by both additive background noise and random phase noise (or slippage between bearing components). To enhance the cyclostationary characteristics for fault detection, it utilizes the phase synchronization property of autocorrelation signals for aligning the cyclostationary signals in the lag domain to achieve an effective ensemble average which allows both types of random influences to be suppressed significantly. As a result, this detector shows very high performance of robustness in extracting the local fault signatures, which is verified by simulation signals and experimental investigations and benchmarked by the recent milestone method of Spectral Correlation (SC).

This paper presents a novel method for diagnosing the gradual deterioration of gears using modulation signal bispectrum (MSB) and vibration measurements. A nonlinear model was derived to understand dynamic forces applied to gears that are excited by quadratic terms, e.g., shaft rotating speeds and gear meshing frequencies. Owing to its sensitivity to those quadratic terms, MSB is powerful in recovering less noisy condition related features from the measured vibration signals, e.g., gear meshing and multiples of shaft rotating speed. This allows a more pronounced representation of gear dynamic forces and makes it more effective for detecting early gear deterioration. The proposed method was verified through a run-to-failure test based on a helical gearbox system. The results show small gears at low-speed stages deteriorate faster and fail at 838 hours. This was because they prone to wear more severe due to poorer lubrication conditions compared with gears at high-speed stages. Moreover, fault detectability of the developed MSB-based method outperforms that of time synchronous averaging (TSA). Compared to TSA, clearer signs of early gear deterioration were captured using MSB, which makes it a more powerful tool for monitoring the condition of gearboxes.

This paper investigates the performance of the conventional bispectrum (CB) method and its new variant, the modulation signal bispectrum (MSB) method, in analysing the electrical current signals of induction machines for the condition monitoring of rotor systems driven by electrical motors. Current signal models which include the phases of the various electrical and magnetic quantities are explained first to show the theoretical relationships of spectral sidebands and their associated phases due to rotor faults. It then discusses the inefficiency of CB and the proficiency of MSB in characterising the sidebands based on simulated signals. Finally, these two methods are applied to analyse current signals measured from different rotor faults, including broken rotor bar (BRB), downstream gearbox wear progressions and various compressor faults, and the diagnostic results show that the MSB outperforms the CB method significantly in that it provides more accurate and sparse diagnostics, thanks to its unique capability of nonlinear modulation detection and random noise suppression.

Online monitoring of the lubrication and friction conditions in internal combustion engines can provide valuable information and thereby enables optimal maintenance actions to be undertaken to ensure safe and efficient operations. Acoustic emission (AE) has attracted significant attention in condition monitoring due to its high sensitivity to light defects on sliding surfaces. However, limited understanding of the AE mechanisms in fluid-lubricated conjunctions, such as piston rings and cylinder liners, confines the development of AE-based lubrication monitoring techniques. Therefore, this study focuses on developing new AE models and effective AE signal process methods in order to achieve accurate online lubrication monitoring. Based on the existing AE model for asperity-asperity collision (AAC), a new model for fluid-asperity shearing (FAS)-induced AE is proposed that will explain AE responses from the tribological conjunction of the piston ring and cylinder. These two AE models can then jointly demonstrate AE responses from the lubrication conjunction of engine ring-liner. In particular, FAS allows the observable AE responses in the middle of engine strokes to be characterised in association with engine speeds and lubricant viscosity. However, these AE components are relatively weak and noisy compared to others, with movements such as valve taring, fuel injection and combustions. To accurately extract these weaker AE's for lubricant monitoring, an optimised wavelet packet transform (WPT) analysis is applied to the raw AE data from a running engine. This results in four distinctive narrow band indicators to describe the AE amplitude in the middle of an engine power stroke. Experimental evaluation shows the linear increasing trend of AE indicator with engine speeds allows a full separation of two baseline engine lubricants (CD-10W30 and CD-15W40), previously unused over a wide range of speeds. Moreover, the used oil can also be diagnosed by using the nonlinear and unstable behaviours of the indicator at various speeds. This model has demonstrated the high performance of using AE signals processed with the optimised WPT spectrum in monitoring the lubrication conditions between the ring and liner in IC engines.