Early Detection of Rolling Bearing Faults Using an Auto-correlated Envelope Ensemble Average

Bearings have been inevitably used in broad applications of rotating machines. To increase the efficiency, reliability and safety of machines, condition monitoring of bearings is significant during the operation. However, due to the influence of high background noise and bearing component slippages, incipient faults are difficult to detect. With the continuous research on the bearing system, the modulation effects have been well known and the demodulation based on optimal frequency bands is approved as a promising method in condition monitoring. For the purpose of enhancing the performance of demodulation analysis, a robust method, ensemble average autocorrelation based stochastic subspace identification (SSI), is introduced to determine the optimal frequency bands. Furthermore, considering that both the average and autocorrelation functions can reduce noise, auto-correlated envelope ensemble average (AEEA) is proposed to suppress noise and highlight the localised fault signature. In order to examine the performance of this method, the slippage of bearing signals is modelled as a Markov process in the simulation study. Based on the analysis results of simulated bearing fault signals with white noise and slippage and an experimental signal from a planetary gearbox test bench, the proposed method is robust to determine the optimal frequency bands, suppress noise and extract the fault characteristics

Vibration Based Centrifugal Pump Fault Diagnosis Based on Modulation Signal Bispectrum Analysis

This paper characterises vibration signals using modulation signal bispectrum method in order to develop an effective and reliable feature sets for detecting and diagnosing faults from both the bearings and impellers in a centrifugal pump. As vibration signals contain high level background noises due to inevitable flow cavitation and turbulences, effective noise reduction and reliable feature extraction are critical procedures in vibration signal analysis. Considering the modulation effect between rotating shaft and vane passing components, a modulation signal bispectrum (MSB) method is employed to extract these deterministic characteristics of modulating components in a low frequency band for diagnosing both the bearing defects and impeller blockages. Experimental results show that the diagnostic features developed by MSB allow impellers with inlet vane damages and bearing outer-race faults to be identified under different operating conditions. Not only does this new method produces reliable diagnostic results but also it needs a bandwidth about 1000Hz, rather than the high frequency bands around 10kHz used by conventional envelope analysis

Monitoring gearbox oil viscosity by means of motor current signal analysis

A number of gearbox failures can be attributed to lubricant related problems. One measure of the condition of gearbox oil is its viscosity. In electrically powered systems, motor current signal analysis allows online estimation of the viscosity of gearbox oil without requiring additional sensors. Previous work on this problem entailed monitoring the power (and change in power) of sidebands of the shaft frequency in the induction motor current spectrum. Sideband frequencies in the current spectrum can however be influenced by other potential problems in the electromechanical system ranging from bearing faults to gearbox teeth damage. Changes in the lubricant viscosity result in changes in the mechanical and thermal losses in the system. These small deviations in the mechanical and thermal losses in the system become visible in the ratio of the electrical energy demanded by the induction motor to the kinetic energy of the rotating mechanical parts. Speed and load invariance can be ensured by normalizing the measured energy ratio with lookup table values obtained when the system attained thermal equilibrium. Speed or load perturbations in the system give rise to small deviations in the normalized energy ratio curve. The distributions of these deviations are significantly different (in a statistical sense) for different oil viscosity values

Effects of Climate Warming on Net Primary Productivity in China During 1961–2010

The response of ecosystems to different magnitudes of climate warming and corresponding precipitation changes during the last few decades may provide an important reference for predicting the magnitude and trajectory of net primary productivity (NPP) in the future. In this study, a process-based ecosystem model, Carbon Exchange between Vegetation, Soil and Atmosphere (CEVSA), was used to investigate the response of NPP to warming at both national and subregional scales during 1961–2010. The results suggest that a 1.3°C increase in temperature stimulated the positive changing trend in NPP at national scale during the past 50 years. Regardless of the magnitude of temperature increase, warming enhanced the increase in NPP; however, the positive trend of NPP decreased when warming exceeded 2°C. The largest increase in NPP was found in regions where temperature increased by 1–2°C, and this rate of increase also contributed the most to the total increase in NPP in China\u27s terrestrial ecosystems. Decreasing precipitation depressed the positive trend in NPP that was stimulated by warming. In northern China, warming depressed the increasing trend of NPP and warming that was accompanied by decreasing precipitation led to negative changing trends in NPP in large parts of northern China, especially when warming exceeded 2°C. However, warming stimulated the increase in NPP until warming was greater than 2°C, and decreased precipitation helped to increase the NPP in southern China

A validated finite element model for predicting dynamic responses of cylinder liners in an IC engine*

Vibration of cylinder liners affects not only engine combustion performances but also tribological behaviour and noise radiations. However, it is difficult to characterize it experimentally due to multiple sources, strong background noise, and nonlinear transfer paths. Therefore, a finite element model is established in this study to predict the dynamic responses of cylinder liners under respective sources. The model takes into account both the characteristics of structural modes and nonlinearities of assembly constraints when selecting adequate elements for efficient computation of the responses under both the highly nonlinear combustion pressure excitations and subsequent piston slap impacts. The predictions are then evaluated against experimental results under different engine operating conditions. In addition, continuous wavelet analysis is employed to process the complicated responses for key response events and their frequency ranges. The results show agreeable correspondences between the numerical predictions and measured vibration signals, paving the way for investigating its effect on combustion and lubrication processes

Dynamic Graph Representation Learning via Graph Transformer Networks

Dynamic graph representation learning is an important task with widespread applications. Previous methods on dynamic graph learning are usually sensitive to noisy graph information such as missing or spurious connections, which can yield degenerated performance and generalization. To overcome this challenge, we propose a Transformer-based dynamic graph learning method named Dynamic Graph Transformer (DGT) with spatial-temporal encoding to effectively learn graph topology and capture implicit links. To improve the generalization ability, we introduce two complementary self-supervised pre-training tasks and show that jointly optimizing the two pre-training tasks results in a smaller Bayesian error rate via an information-theoretic analysis. We also propose a temporal-union graph structure and a target-context node sampling strategy for efficient and scalable training. Extensive experiments on real-world datasets illustrate that DGT presents superior performance compared with several state-of-the-art baselines

Extraction of Information from Vibration Data using Double Density Discrete Wavelet Analysis for Condition Monitoring

Many condition monitoring (CM) techniques have been investigated for the purpose of early fault detection and diagnosis in order to avoid unexpected machine breakdowns. However, non-stationary and non-linear characteristics of vibration data can make the signal analysis a challenging task. Multiresolution data analysis approaches have received significant attention in recent years and are widely applied to analyse non-stationary and non-linear data. Double-Density Discrete Wavelet Transform (DD-DWT), which was originally developed for image processing, is proposed and investigated in this paper for effectively extracting diagnostic features from the vibration measurements. DD-DWT has the merits of nearly shift-invariant and less frequency aliasing which and allows the effective extraction of non-stationary periodic peaks, compared with the undecimated DWT. Techniques based on thresholding of wavelet coefficients are gaining popularity for denoising data. The implementation of global, level-dependent, and subband-dependent thresholding based methods are investigated and implemented on the selected wavelet coefficients in order to denoise and enhance the periodic and impulsive fault features. The performance of the proposed method has been evaluated against DWT using both simulated data and experimental datasets from defective tapered roller bearings. Results, using the harmonic to signal ratio (HSR) as a measure, have demonstrated that DD-DWT outperforms conventional DWT in feature extraction and noise suppression. As a result, the proposed method is robust and effective in fault detection and diagnosis

A Componential Coding Neural Network based Signal Modelling for Condition Monitoring

Many condition monitoring (CM) techniques have been investigated for early fault detection and diagnosis in order to avoid unexpected breakdowns due to machinery failures. However, manual techniques require well-skilled labours which will increase the cost of the monitoring process and may not always be available at the site. One of the most promising approaches is to automate the monitoring process using artificial intelligence (AI) techniques. However, the majority of AI-based techniques have been developed in CM for the post-processing stage, whereas the critical tasks including feature extraction and selection are still manually processed. This study focuses on the extending AI techniques in all phases of CM process by using a Componential Coding Neural Network (CCNN) which has been found to have unique properties of being trained through unsupervised learning, capable of dealing with raw data sets, translation invariance and high computational efficiency. These advantages of CCNN make it particularly suitable for automated analysis of the vibration data arisen from typical machine components such as the rolling element bearings which exhibit periodic phenomena with high non-stationarity and strong noise contamination. The CCNN was evaluated using both simulated and experimental data collected from a healthy and two defective tapered roller bearings under different operating conditions. Both of the results showed the capability of CCNN in detecting the initial anomalies of roller element bearings

Regional Geological Survey of Hanggai, Xianxia and Chuancun, Zhejiang Province in China

This Open Access book introduces readers to the regional geology of Hanggai, Xianxia and Chuancun, the area between China's northern Zhejiang Province and southern Anhui Province and explores the strata, magmatic rocks and tectonic structures in 1:50,000 scale geological maps. Based on studies of multiple stratigraphic divisions, the standard stratigraphic section of the upper Ordovician Hirnantian in the lower Yangtze region is established, revealing for the first time numerous “Burgess Shale-type” sponge fossils in Hirnantian strata and identifying 10 grapholite fossil belts and various fossil categories, including chitin, trilobites, gastropods, brachiopods, and cephalopods. Moreover, the book identifies for the first time Late Ordovician volcanic events in northern Zhejiang province. The work represents a major contribution to research on Paleozoic strata in the Lower Yangtze region, and sheds new light on understanding the Hirnantian glacial event and biological extinction event in South China by providing a high-precision time scale. In addition, the book opens an important avenue for future research on sponge evolution after the Cambrian life explosion. As such, it offers a unique and valuable asset for researchers and graduate students alike