The transparent nanocrystalline thin films of undoped zinc oxide and Mn-doped (Zn1−xMnxO) have been deposited on glass substrates via the sol–gel technique using zinc acetate dehydrate and manganese ...chloride as precursor. The as-deposited films with the different manganese compositions in the range of 2.5–20at% were pre-heated at 100°C for 1h and 200°C for 2h, respectively, and then crystallized in air at 560°C for 2h. The structural properties and morphologies of the undoped and doped ZnO thin films have been investigated. X-ray diffraction (XRD) spectra, scanning electron microscopy (SEM), atomic force microscopy (AFM), and X-ray photoelectron spectroscopy (XPS) were used to examine the morphology and microstructure of the thin films. Optical properties of the thin films were determined by photoluminescence (PL) and UV/Vis spectroscopy. The analyzed results indicates that the obtained films are of good crystal quality and have smooth surfaces, which have a pure hexagonal wurtzite ZnO structure without any Mn related phases. Room temperature photoluminescence is observed for the ZnO and Mn-doped ZnO thin films.
Display omitted The transparent nanocrystalline thin films of undoped zinc oxide and Mn-doped (Zn1-xMnxO) and their optical properties are reported.
•The transparent nanocrystalline ZnO thin films with different Mn doping contents were prepared by a sol–gel technique.•The surface roughness and average crystallite size of thin films are sensitive to Mn doping contents.•A board UV peak ascribed to the free exciton emission is observed in the undoped or doped ZnO thin film.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
•Addressed the mode mixing and/or integrity issues due to spectral overlaps.•Fine time–frequency resolution, and free of both outer and inner interferences.•Effective in revealing time-varying ...features of nonstationary signals.•Validated by experimental test and on-site measurement of typical rotating machines.
Vibration signal analysis provides an effective approach for condition monitoring and fault diagnosis of rotating machines. Under time-varying conditions, vibration signals feature nonstationarity, consist of multiple frequency components, and usually have spectral overlaps. Adaptive mode decomposition methods can extract mono-components from a given multi-component signal to meet the requirement for estimating instantaneous frequency. Among them, the recently proposed methods, including empirical wavelet transform, variational mode decomposition and Fourier decomposition method, outperform the classic empirical mode decomposition in terms of rigorous mathematical formulation. Nevertheless, for multi-component signals with spectral overlaps, these three methods are subject to mode mixing and/or integrity issues, and fail to extract true mono-components, because they essentially separate mono-components based on spectral segmentation. In this paper, we propose a framework by exploiting the capability of angular resampling to address the mono-component overlapping issue. This methodology can separate true mono-components, thus facilitating accurate estimation of instantaneous frequency through Hilbert transform and generating perfect time–frequency representations (TFRs). Firstly, angular resampling is employed to make the constituent mono-components well separable in the frequency domain. Then, true mono-components are separated through adaptive mode decomposition. Next, informative mono-components are selected for further processing based on the prominent order information, and the selected mono-components are mapped into the time domain according to the relationship between the equal time and equal angle sampling. Finally, the instantaneous frequency and amplitude envelope of the recovered mono-components are calculated via Hilbert transform, and the TFR of raw signal is obtained by superposing the TFRs of all recovered mono-components. By doing so, the TFR achieves a fine time–frequency resolution and is free of both outer and inner interferences. The proposed methodology is demonstrated by simulated signal analysis, and further validated using the vibration data sets of three typical rotating machines (including a planetary gearbox in a wind turbine drivetrain, a civil aircraft engine and a hydraulic turbine rotor). The analysis results show its excellent capability to reveal the time-varying features of rotating machinery nonstationary signals.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Wind turbine planetary gearboxes often run under nonstationary conditions due to volatile wind conditions, thus resulting in nonstationary vibration signals. Time-frequency analysis gives insight ...into the structure of an arbitrary nonstationary signal in joint time-frequency domain, but conventional time-frequency representations suffer from either time-frequency smearing or cross-term interferences. Reassigned wavelet scalogram has merits of fine time-frequency resolution and cross-term free nature but has very limited applications in machinery fault diagnosis. In this paper, we use reassigned wavelet scalogram to extract fault feature from wind turbine planetary gearbox vibration signals. Both experimental and in situ vibration signals are used to evaluate the effectiveness of reassigned wavelet scalogram in fault diagnosis of wind turbine planetary gearbox. For experimental evaluation, the gear characteristic instantaneous frequency curves on time-frequency plane are clearly pinpointed in both local and distributed sun gear fault cases. For in situ evaluation, the periodical impulses due to planet gear fault are also clearly identified. The results verify the feasibility and effectiveness of reassigned wavelet scalogram in planetary gearbox fault diagnosis under nonstationary conditions.
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DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
•Both amplitude and phase modulations are considered in current signal model.•Time-varying spectrum of squared space vector modulus is derived.•It overcomes difficulty due to dominant power supply ...frequency and intricate sidebands.•It is effective in extracting the time-varying fault characteristic frequencies.
Motor current signature analysis is a promising technique for electromechanical system fault detection, and has been studied under steady states. In this paper, a planetary gearbox fault diagnosis method under variable speed conditions using stator current signal is proposed. In order to thoroughly understand the frequency characteristics of current signals, an analytical amplitude modulation and phase modulation (AM-PM) current model considering gear fault modulation effects is presented. Two more aspects of endeavor are made to highlight gear fault signatures in stator current signals in context of inconspicuousness and time variability. Firstly, to address the sideband complexity and power supply dominance issues inherent with stator current signals, squared space vector modulus (SVM) together with its time-varying spectral characteristics in planetary gearbox fault cases under variable speed conditions are mathematically derived. Secondly, to reveal time-varying fault features in details, polynomial chirplet transform (PCT) is improved by iterative algorithm, and merits of fine time-frequency resolution and cross term free nature are achieved. The effectiveness of the proposed method is illustrated by numerical simulation, and is further validated by lab experiments on a real world 4 kW induction motor driven planetary gearbox test rig.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract
Vibration signals of planetary gearboxes have complex components and time-varying characteristics. As the unstable operation of planetary gearboxes leads to unbalanced data distribution ...within vibration signals, it is difficult to extract gearbox fault information hidden in a large amount of data. Therefore, fault diagnosis of planetary gearboxes under nonstationary conditions is highly challenging. For the past few years, intelligent diagnosis methods have been extensively studied in the fault diagnosis field. However, inappropriate signal representations, inadequate training samples, and data differences increase the difficulty of diagnosing planetary gearbox faults. To address the above issues, this paper proposes an intelligent diagnostic framework based on time–frequency features and a deep residual joint subclass alignment transfer network (DSATN) for planetary gearbox fault diagnosis under nonstationary conditions. One-dimensional vibration signals are converted into time–frequency representation through signal processing techniques to reflect the variation of vibration frequency components within the time–frequency domain with time. During network training, the DSATN evaluates the data distributions between relevant subclasses in source and target tasks by using the local maximum mean discrepancy. Also, it utilizes a nonlinear transformation to align the global data distributions between both tasks, thus improving the generalization of the trained model for small sample sets. The proposed method is validated through planetary gearbox experiments and achieves good fault classification in the time–frequency domain of nonstationary vibration signals. Different gear and planet bearing fault categories are successfully identified.
Multicomponent nonstationary signals with close instantaneous frequencies (IFs) are commonly encountered in rotating machinery condition monitoring and fault diagnosis. It is very challenging to ...accurately reveal the physical natures of such signals. To address the issue, this article presents the proportion-extracting chirplet transform (PECT). In the PECT, the proportional kernel functions can match with the time-varying patterns of all constituent components. This enables the PECT to eliminate the artifacts caused by spectral overlaps in short-time windows and to achieve fine frequency resolution. Meanwhile, the corresponding proportional frequency shifting operators ensure satisfactory time resolution. Therefore, the PECT provides high time-frequency resolution and suffices to characterize the time-frequency structures of nonstationary signals with close IFs. The experimental and in situ measured data analysis results of typical rotating machinery validate the effectiveness of the PECT.
Abstract
Intelligent diagnosis methods based on big data have been extensively applied in the fault diagnosis of rotating equipment such as planetary gearboxes. Most of these methods usually satisfy ...the condition of independent identically distribution among the training and diagnosis data. However, the data distribution in the actual diagnosis task struggles to satisfy the above conditions due to the lack of fault data, missing label information and the feature differences within different signals, thus increasing the difficulty of cross-condition fault diagnosis in small sample sets. Therefore, we propose a dynamic adversarial balance adaptation method with multi-label information confusion (MLC-DABA) for diagnosing planetary gearbox faults under time-varying conditions. In the signal preprocessing process, we transform the nonstationary timing signals into two-dimensional time-frequency matrices for the feature learning of networks, which avoids the frequency characteristic mess caused by frequency overlap. Moreover, we adopt a balance adaptation algorithm to dynamically evaluate the feature distribution between source and target tasks through the domain labels and category labels, thus establishing a balanced adaptation relationship between the feature distributions of both tasks. This dynamic adversarial training mechanism can tap more domain-invariant feature information to measure the distribution distance among tasks, thus closing the feature differences in different tasks and increasing the generalization of source tasks to the data distribution in target tasks. The proposed method is verified in planetary gearbox experiments. Experimental analysis results indicate that the diagnostic performance of MLC-DABA outperforms other comparison methods in terms of accuracy and training robustness.
The vibration signals from complex structures such as wind turbine (WT) planetary gearboxes are intricate. Reliable analysis of such signals is the key to success in fault detection and diagnosis for ...complex structures. The recently proposed iterative atomic decomposition thresholding (IADT) method has shown to be effective in extracting true constituent components of complicated signals and in suppressing background noise interferences. In this study, such properties of the IADT are exploited to analyze and extract the target signal components from complex signals with a focus on WT planetary gearboxes under constant running conditions. Fault diagnosis for WT planetary gearboxes has been a very important yet challenging issue due to their harsh working conditions and complex structures. Planetary gearbox fault diagnosis relies on detecting the presence of gear characteristic frequencies or monitoring their magnitude changes. However, a planetary gearbox vibration signal is a mixture of multiple complex components due to the unique structure, complex kinetics and background noise. As such, the IADT is applied to enhance the gear characteristic frequencies of interest, and thereby diagnose gear faults. Considering the spectral properties of planetary gearbox vibration signals, we propose to use Fourier dictionary in the IADT so as to match the harmonic waves in frequency domain and pinpoint the gear fault characteristic frequency. To reduce computing time and better target at more relevant signal components, we also suggest a criterion to estimate the number of sparse components to be used by the IADT. The performance of the proposed approach in planetary gearbox fault diagnosis has been evaluated through analyzing the numerically simulated, lab experimental and on-site collected signals. The results show that both localized and distributed gear faults, both the sun and planet gear faults, can be diagnosed successfully.
•The iterative thresholding method is applied to analyzing complex signals.•A criterion is proposed to estimate the number of sparse components.•The method is effective in extracting feature frequencies and suppressing noise.•It is evaluated using simulated, lab experimetnal, and on-site measured signals.•It is effective in diagnosing both the local and distributed gear faults.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
•Review vibration based fault diagnosis methods of wind turbine planetary gearbox.•Fundamental analysis of the planetary gearbox vibration response are reviewed.•Fault enhancement and operation ...pattern effect elimination are reviewed.•Feature extraction and fault diagnostics strategy are reviewed.•Prospects of vibration based fault diagnosis methods are discussed.
As one of the most immensely growing renewable energies, the wind power industry also experiences a high failure rate and operation & maintenance cost. Therefore, the condition monitoring and fault diagnosis of a wind turbine (WT) generator set are highly needed. Among different components of a WT generator set, WT planetary gearbox plays a crucial role in transmission and leads to relatively higher failure rate and longer downtime. Towards this, a number of studies have been reported in both the academic journals and conference proceedings. This paper provides a systemic and pertinent state-of-art review on WT planetary gearbox condition monitoring techniques on the topics of fundamental analysis, signal processing, feature extraction, and fault detection. Moreover, a few valuable open issues are pointed out and potential research directions are suggested.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
An emerging outbreak of Covid-19 has now been detected across the globe. Given this pandemic condition, the robust estimation reports are urgently needed. Therefore, this study aims to analyze the ...impacts of community mobility (before, during, and after the lockdown period) on the spread of the Covid-19 in Jakarta, Indonesia. The secondary data was derived from surveillance data for Covid-19 daily cases from the Health Office of DKI Jakarta Province and the Ministry of Health. The community mobility indicators were retrieved from the Google website. Our results showed that in the pre-lockdown period, the Covid-19 daily cases rapidly increased, while community mobility significantly dropped. The increasing number of Covid-19 daily cases was significantly affected by the number of Covid-19 tests per day rather than community mobility. During the restriction period, the number of Covid-19 tests per day, and community mobility statistically affected the decreasing number of Covid-19 daily cases. Meanwhile, after the lockdown period, the number of Covid-19 daily cases rapidly increased, which significantly has a direct relationship with the increasing level of community mobility. Overall, community mobility and the number of tests per day are the essential variables that explain the number of Covid-19 daily cases in Jakarta, Indonesia. Additionally, this study did not observe any impact of average air temperature and air pollution on the spread of Covid-19. This study figures out that community mobility could potentially explain the progression of Covid-19.
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