Frequency Of Defects Research Articles

Investigation of electric field effect on defects in GaAsN by admittance spectroscopy

• Electric-field dependent properties of defects in GaAsN. • Temperature dependent properties of defects in GaAsN. • Extraction of defect properties without the Arrhenius plot. We use the Arrhenius transformation and matching (ATM) method to extract activation energy and attempt-to-escape frequency of the nitrogen-related defect (E1) in GaAsN from admittance spectroscopy measurements taken at various temperature and electric field. The ATM method is equivalent to the conventional Arrhenius plot and line-fitting (APL) method for Arrhenius processes with temperature-independent E a and ν 0 (e.g., E1 in a low electric field and at high temperatures) and advantageous for non–Arrhenius processes with temperature-dependent E a and ν 0 , (e.g., E1 in a high electric field and at low temperatures), where the APL method is not reliable. The ATM method enables a detailed account on how E a evolves with the temperature and the electric field in a continuously varying experimental space, which is not possible with the APL method. The evolution of carrier emission behaviors from the E1 defect and a neighboring E2 defect provides experimental evidence of field-induced metastability possibly due to the interaction of two defects with close atomic origins or spatial locations.

Follow-Up of Newborns with Neural Tube Defects: A Single Center Experience

OBJECTIVES: Neural tube defects, defined as the developmental malformations of the central nervous system, are seen in our country at a rate of three per thousand on average. Our study aims to retrospectively evaluate patients with neural tube defects hospitalized in our intensive care unit for the last decade. STUDY DESIGN: Demographic data, prenatal-maternal characteristics, surgical operation time, intensive care follow-up, and long-term neurological-developmental results of these patients were also evaluated. RESULTS: Eighty-six percent (n=31) of the patients were diagnosed prenatally. Arnold Chiari type 2 and type 3 malformation was 64% (n=23) and 3% (n=1), respectively. Eighty-nine (n=32) percent of these patients had meningomyelocele, whereas 90% (n=27) of them had defects in the lumbosacral region. The patients were operated on the median postnatal 9th day (4-27). Compared patients who were operated on in the 1st week to the patients operated on following the first week, any difference was not detected in terms of neuromotor and cognitive development, and urinary/stool incontinence frequency. Of 28 patients whose long-term follow-up was questioned, 7 (25%), 25 (89%), and 16 (57%) were able to walk, talk, and go to school along with being compatible with their peers, respectively. CONCLUSION: We presented our center's experience to make a significant contribution to the decrease of the neural tube defect frequency, which is commonly seen in our country compared to developed countries.

Transient-extracting transform based on non-convex sparse regularization: A useful scheme for bearing fault feature enhancement

Periodic transient shocks of vibration signals often reflect the failure of mechanical components. With the time-frequency (TF) distribution (TFD), Transient-extracting transform (TET) can descript and extract the fault-related transient component effectively. Nevertheless, vibrations collected from practical sources are always contaminated by strong background noise, thus the transient shocks are often submerged by noise components. Therefore, how to effectively extract the weak transient component from noise is an important task to achieve the bearing fault diagnosis. Considering the above issue, this paper developed a generalized mini-max concave (GMC) penalty based TET (GMCTET) for bearing fault feature enhancement. Specifically, the GMC penalty based-sparse regularization is firstly applied to the raw signal. Benefiting from the good ability of GMC penalty-based sparse regularization in both sparsity and signal fidelity. The transient shocks are initially presented, and the loss of energy amplitude of the raw signal during the processing is reduced simultaneously. Next, TET can well characterize these transient shocks via a highly readable TFD, and the transient component is extracted based on the TF coefficients. The shock structures in transient component are further highlighted after TET treatment. Finally, the bearing fault frequencies are identified through the spectrum of the extracted transient component. The effectiveness of GMCTET is verified from the comparative analysis results of the simulation and experimental studies.

Evaluation of Rolling Bearing Performance Degradation Based on Comprehensive Index Reduction and SVDD

The evaluation of rolling bearing performance degradation has important implications for the prediction and health management (PHM) of rotating equipment. A method for evaluation of rolling bearing performance degradation based on comprehensive index reduction and support vector data description (SVDD) is proposed in this study. Firstly, the improved variational mode decomposition (VMD) method was used to decompose vibration signals, and the defect frequency amplitude ratio index which is sensitive to early faults is extracted. Secondly, a comprehensive feature index set of rolling bearings is constructed by combining traditional time-domain and time–frequency-domain indexes, and the main features are extracted by the dimensionality reduction algorithm of locally linear embedding (LLE). Finally, the SVDD evaluation model was utilized to characterize and evaluate the rolling bearing lifetime degradation process using the distance from the test sample to the trained hypersphere center. Results showed that the proposed comprehensive degradation index can accurately detect the occurrence of early weak fault stage of rolling bearings and objectively reveal the performance degradation process of rolling bearings.

Development of An Artificial Neural Network Based Time-Series Fault Predictive Model for Power System

Electrical power is one of the most important forms of energy which is needed in almost every field of human endeavour. However, increase in size of electrical power structure without proper planning has negative effects on power system supplied to end users, thereby increasing the fault level of the network. This research paper therefore, developed an Artificial Neural Network based Time Series (ANN-TS) fault predictive model for forecasting of fault levels in power system. In this paper, ANN-TS model was trained with three years (2015-2017) outage fault frequency and fault duration data obtained from Ayede 132/33 kV transmission substation of Transmission Company of Nigeria (TCN) in Ibadan using Resilient Back-Propagation (RBP) algorithm and simulation was carried out in MATLAB environment using Mean Absolute Percentage Error (MAPE) as performance metric. The model was used to predict yearly fault frequency and fault duration for twenty-three years (2018-2040). The results of the fault frequency forecast showed that monthly forecast graphs were overlapped. Yearly MAPE varied between 0.004 % and 25 %, and the feeders’ average MAPE was between 6 % and 10 %. In fault duration, the graphs followed the same pattern in nearly all the paths of the graphs, the yearly MAPE varied between 0.001 % and 25.54 % and the feeders’ average MAPE varied between 6 % and 11 %. The model produced a fairly accurate forecast according to the criteria of MAPE. The average overall MAPE of each feeder was between 6 % and 10 % which indicated between 90 % and 94 % accuracy of the model. Therefore the ANN-TS model is effective for fault prediction in reference time series. Keywords :Electrical Power, Fault, Artificial Neural Network based Time Series, Resilient Back-Propagation, Mean Absolute Percentage Error, Forecasting, Transmission substation. DOI: 10.7176/JETP/12-3-01 Publication date: August 31 st 2022

A Generalized Method for In-Process Defect Detection in Friction Stir Welding

Friction stir welding (FSW) is an advantageous solid-state joining process that is suitable for many materials in multiple industries. In an industrial setting, manufacturers are actively seeking faster welding speeds to increase throughput. Increasing welding speed limits the size of defect-free parameter windows, which may increase the frequency of defects. The push for faster welding speeds emphasizes the need for economical non-destructive evaluation (NDE) for FSW, like any other type of welding. This work introduces a generalized defect detection method that recognizes the stochastic nature of the FSW process, and that can be generally applied to FSW of a material across a dynamic range of process parameters and welding conditions. When applied to aluminum friction stir-welded blanks at speeds ranging from 1500 to 3000 mm/min with varying ranges of tool tilts, the methodology proved 100% effective at positive detection when defects were present with zero scrap rate. Furthermore, additional development demonstrated the proposed stochastic approach can be used to detect the spatial location of a defect within a weld with 94% detection accuracy and a 4.2% scrap rate.

An Early Fault Diagnosis Method for Ball Bearings of Electric Vehicles Based on Integrated Subband Averaging and Enhanced Kurtogram Method

Faults of mechanical transmission systems generally occur in the rotating bearing part at high speeds, which causes problems such as performance degradation of transmission, generation of noise or vibration, and additional damage to connected adjacent systems. In this way, faults cause adverse effects to the entire system, such as deterioration and damage. The early detection and correction of bearing problems allows for improved system safety and the reduction of maintenance costs, resulting in efficient system operation. As a result, a variety of methods have been developed by many researchers in order to diagnose bearing mechanical defects, and one of the most representative methods is applying various signal processing techniques to vibration data. Wavelet packet transform (WPT) and kurtogram were used in this study to identify the frequency band that contained the fault component, and the enhanced kurtogram technique was used to analyze the fault. A technique for minimizing the effect of intermittent abnormal peak components caused by noise and external influences has been presented using sub-band averaging to detect early fault frequency component detection and fault development. Using the technique proposed in this study, the state of the bearing based on the degree of fault was evaluated quantitatively, and it was demonstrated experimentally that the bearing fault frequency could be detected at an early stage by the filtered data. In a situation where it is difficult to accept all the detailed design specifications and operating conditions of the complex mechanical systems at industrial sites, determining the degree of fault with simple time-series data and detecting fault components at an early stage is a practical analysis technique for fault diagnosis in the industrial field using various rotating bodies.

Empirical wavelet decomposition and BFindex for early detection of bearing defects

In this paper, a signal processing scheme is developed for early detection of bearing defects .This scheme combines Empirical Wavelet Decomposition (EWD) technique and Frequency Weighted Energy Operator (FWEO). EWD employs an adaptive process that is able to exploit the frequency information contained in the signal and decompose it into a finite number of Empirical Modes (EMs). However, this technique still faces the problem of how to optimally divide the Fourier spectrum during the application process to extract empirical modes with physical meanings from the vibration signal. To overcome this problem we propose a new segmentation method based on the research of the frequency resonance band in the power spectrum which is excited by defect impacts. In the other hand, identifying the mode that carries the fault information among all the obtained modes is the key to successful application of EWD technique. In this paper we propose a new criterion named Bearing Fault index (BFindex) for automatically selecting the correct mode. Then, this mode is demodulated using FWEO to separate the defect frequency from the natural frequency excited by the impacts. The proposed scheme is evaluated using real bearing vibration signals with inner race and outer race defects. The experimental results confirmed its ability in detecting fault characteristic frequencies, which validates its effectiveness in the diagnosis of bearing defects.

Childhood stress and developmental instability: Comparing microscopic enamel defects and cranial fluctuating asymmetry in a colonial Mexican sample

AbstractObjectivesRecent tests of the relationship between enamel defects, an indicator of early life stress, and fluctuating asymmetry (FA), a measure of cumulative developmental instability, have produced equivocal results. This may be because they use methods that underestimate the number of defects. We reinvestigate this relationship using high‐resolution microscopy images of tooth surfaces. Further, we test the hypothesis that developmental stresses during the earliest period of tooth development have a greater impact on FA than stresses during subsequent periods.Materials and MethodsThe sample consists of 48 individuals from two colonial‐era cemeteries in Mexico City. Using one canine from each individual, we created photomontages using a 2D light microscope. Tooth crowns were divided into three developmental periods. Defects were identified as widely spaced perikymata using moving z‐scores. We compared defect presence, frequency, and timing to FA calculated from 11 landmarks using geometric morphometrics from 3D models of each cranium.ResultsBoth the presence and frequency of enamel defects are significantly associated with an increase in FA. Contrary to our expectations, defects that formed in the latest developmental period (4–5.5 years) were uniquely associated with an increase in FA, relative to the other two periods.DiscussionThe presence and frequency of stress during childhood increases developmental instability. The time between 4 and 5.5 years may be a sensitive window of development in which disruptions to growth have a long‐lasting effect on developmental instability. Results indicate that research on developmental stress should consider the timing and frequency of stress events to more fully understand their impact on later life.

Bearing Fault Diagnosis Based on Stochastic Resonance and Improved Whale Optimization Algorithm

In light of the problem of difficult model parameter selection and poor resonance effects in traditional bearing fault detection, this paper proposes a parameter-adaptive stochastic resonance algorithm based on an improved whale optimization algorithm (IWOA), which can effectively detect bearing fault signals of rotating machinery. First, the traditional WOA was improved with respect to initial solution distribution, global search ability and population diversity generalization, effectively improving the global convergence of the WOA. Then, the parameters of the bistable stochastic resonance model were optimized using the improved WOA, and adaptive adjustment of the stochastic resonance parameters was realized. Finally, the Case Western Reserve University bearing data set and the XJTU-SY bearing data set were used as fault data for the actual bearing to be tested for experimental verification. The signal-to-noise ratios of the detected fault frequencies for the above two data sets were −20.5727 and −21.1289, respectively. Among the algorithms compared, the IWOA proposed in this paper had the highest signal-to-noise ratio of the detected fault frequencies. The experimental results show that the proposed method can effectively detect a weak bearing fault signal in enhanced noise.

Prediction of broken rotor bar in induction motor using spectral entropy features and TLBO optimized SVM

The information of the fault frequency characteristics is of great importance for all associated fault diag nostics. This requires a high-resolution spectrum analysis to achieve efficient monitoring of machinery faults, especially while diagnosing rotor bar breakage under light load conditions, because the fault frequencies almost overlap with the fundamental. In this context, rather than looking for frequencies associated with rotor faults, several frequency bands are observed separately in terms of the entropy contained within these bands. First, the motor current signal has been divided into several frequency bands using the continuous wavelet transform (CWT), and the spectral entropy is calculated from each band as the features to describe the rotor condition. Principal component analysis (PCA) has been used as a feature reduction tool, and the features projected onto the first two principal components have been fed into the SVM for inference. SVM is a supervised learning method used for classification and regression analysis. To improve classification performance, a radial basis function (RBF) kernel has been employed, and to find the optimal value of the kernel parameters, a metaheuristic approach, namely teaching learning-based optimization (TLBO), is utilized. The ANSYS 2D workbench is used to simulate the finite element model (FEM) of an induction motor with broken rotor bars, and the efficacy of the proposed method is then tested using simulated data. To investigate the robustness of the proposed approach, white Gaussian noise has also been added to the simulated data, and the performance of the SVM is tested with these spectral features.

Enhanced demodulation band selection based on Operational Modal Analysis (OMA) for bearing diagnostics

Vibration time signals emitted by a defective rolling element bearing are often used to determine its health condition. For real-world measurements these fault signals may be weak compared to the remaining content in the measured signal causing increased difficulties to fault detection. One way of enhancing the fault detection is by demodulation at a filtered frequency band in which the Signal-to-Noise-Ratio (SNR) of the fault signal is high. In the present paper, we propose a method that uses the resonances obtained from Operational Modal Analysis (OMA) as center frequencies for the filter bands as it is assumed that the characteristic bearing fault frequencies modulate resonances or carrier frequencies close to them. An indicator is used to determine which of the resonances from the OMA enhance the fault signal the most and this selection is dependent on the bandwidth as well. The presented method is applied on signals from two experimental cases, an experimental test rig of a planetary gearbox and a wind turbine gearbox, and is compared to the Fast Kurtogram and Log-Cycligram to assess its performance for band selection. The results show that the presented method determines a suited band for band pass filtering which enhances the fault detection at the two complex use cases.

Domain Knowledge Informed Unsupervised Fault Detection for Rolling Element Bearings

Early and accurate detection of rolling element bearing faults in rotating machinery is important for minimizing production downtime and reducing unnecessary preventative maintenance. Several fault detection methods based on signal processing and machine learning methods have been proposed. Particularly, supervised, data-driven approaches have proved to be very effective for fault detection and diagnostics of rolling element bearings. However, supervised methods rely heavily on the availability of failure data with volume, variety and veracity, which is mostly unavailable in industry. As an alternative data-driven strategy, unsupervised methods are trained on healthy data only and do not require any failure data. In contrast to supervised and un-supervised data-driven models, physics-based and phenomenological models are based on domain knowledge and not on historical data. Although these models are useful for studying the way in which damage is expected to manifest in a measured signal, they are difficult to calibrate and often lack the fidelity required to model reality. In this paper, an unsupervised data-driven anomaly detection method that exploits informative domain knowledge is proposed. Hereby, the versatility of unsupervised data-driven methods are combined with domain knowledge. In this approach, supplementary training data is generated by augmenting healthy data towards its possible future faulty state based on the characteristic bearing fault frequencies. Both healthy and augmented squared envelope spectrum data is used to train an autoencoder model that includes regularisation designed to constrain the latent features at the autoencoder bottleneck. Regularisation in the autoencoder loss enforces that the expected deviation of the healthy latent representation towards the augmented latent representation at dam aged conditions, is constrained to be maximally different for different fault modes. Consequently, the likelihood of a new test sample being healthy can be evaluated based on the projection of the sample onto an expected failure direction in the latent representation. A phenomenological and experimental dataset is used to demonstrate that the addition of augmented training data and a specialized autoencoder loss function can create a separable latent representation that can be used to generate interpretable health indicators.

Selective weighted multi-scale morphological filter for fault feature extraction of rolling bearings

Morphological filtering shows effectiveness in vibration signal analysis because of its simplicity and efficiency. Considering that different structural elements have different effects on filtering results, a new multi-scale morphological filtering (MMF) method called selective weighted multi-scale morphological filter (SWMMF) is developed for integrating results of different scales based on adaptive weighting strategy. Firstly, four morphological operators (dilation–closing, closing–dilation, erosion–opening and opening–erosion) are integrated into a new combination difference morphological filter to strengthen effect of faulty component extraction. Secondly, this new morphological filter is further extended to multiple scales in order to overcome limitation of single scale filter. Finally, the filtered results of different scales are adaptively combined by using the whale optimization algorithm (WOA)-based selective weighting method. The effectiveness of multi-scale filter and selective weights is proved by comparing with single-scale and average weighting filter on simulation and real-world cases (bearing vibration signals with different defects). The testing results on vibration signals indicate that SWMMF is able to extract effectively defect frequency and the corresponding multiplication frequencies from bearing vibration signals with heavy noise. The testing results illustrate that SWMMF outperforms other representative MMFs (e.g., weighted multi-scale morphological gradient operator (WMMG), weighted multi-scale difference operator (WMDIF), weighted multi-scale average operator (WMAVG)) on impulsive feature extraction of bearing vibrations signals with various defects. Moreover, it is demonstrated that SWMMF has good applicability in bearing fault diagnosis due to setup of adaptive weights and selection of structure element.

CONGENITAL MALFORMATIONS – MARKER OF XENOBIOTICS INFLUENCE OF LOW-INTENSITY

Formation of congenital malformations in many respects occurs under the infl uenceof unfavorable stimuli of the external environment. In its turn, many of the so-called«watch» malformations may be the markers of ecological troubles.Objective – to analyse the frequency of malformation origin in fetuses and newborns forthe period of 2004-2014 under conditions of their parents living in alternative as to thepollution districts of the Chernivtsi region.Material and methods. Complex anthropogenic infl uence on the organism was assessedtaking into account the bioavailability of pollutants in the soil, water and air in certainareas of the region according to the value of the environmental risk factor (CER) proposedby us. Data analysis concerning the general number of newborns with congenitalmalformations for the period 2004-2018 year was carried out. The obtained results of theresearch were analyzed be means of the computer package «Statistica 6» Stat Soft andExcell XP for Windows on a personal computer using parametric and non-parametricmethods of calculation.Results. It has been shown that under conditions of the parents living in areas of ecologicaltrouble(CER≥2.0), the frequency of the development defects in their newborns constituted48.0%, and the total number of such defects was 50.36%. In the case of the parents livingin more favorable environmental conditions (CER<2.0), the frequency of malformationsdevelopment constituted correspondingly 36.3% and 39.0% (p<0.05). Correlationof chances (CCh) of the congenital malformations in newborns, whose parentspermanently lived in the zones of ecological risk in relation to the group of comparison became 1.62 (95% CI: 1-2.6) CI (confi dential interval), and for the native populationCCh was 1.59 (95% CI: 1-2.8).Conclusion. In case of the prolong parents’residence in the regions with high riskof infl uence on their organism of ecological factors of low density, gross (watch)malformations, recommended to the obligatory register EUROCAT, were formed in thenewborns more often than in the group of comparison.

Presenting Patterns of Genetically Determined Developmental Encephalopathies With Epilepsy and Movement Disorders: A Single Tertiary Center Retrospective Cohort Study.

BackgroundThis paper aimed to evaluate the frequency of observation of genetically determined developmental encephalopathies with epilepsy and movement disorders in a specialistic center, the distribution of etiologies and presenting clinical hallmarks, and the mean times for the achievement of molecular genetic diagnosis.Patients and MethodsRetrospective data about clinical phenotypes, etiology, and diagnostic pathways were collected in all the genetically confirmed patients with developmental encephalopathies with epilepsy and movement disorders referred to our institution between 2010 and 2020. The cohort was divided into two groups according to the predominant movement disorder type: 1) Group A: patients with hyperkinetic movement disorders; 2) Group B: patients with hypokinetic movement disorders. Both groups were analyzed in terms of developmental, epileptic, and movement disorder phenotypes.ResultsThe cohort included 69 patients (Group A = 53; Group B = 16). The etiological spectrum was heterogeneous with a predominance of Rett and Angelman syndrome in Group A and neurodegenerative disorders in Group B. A moderate/severe intellectual disability was assessed in 58/69 patients (mean age at the first signs of developmental impairment = 1,87 ± 1,72 years). Group A included patients with an earlier onset of epileptic seizures (2,63 ± 3,15 vs. 4,45 ± 5,55 years of group B) and a predominant generalized motor semiology of seizures at the onset. Focal seizures were the main initial epileptic manifestations in Group B. Seizures were noticed earlier than movement disorders in Group A while the opposite occurred in Group B. A higher increase in molecular genetic diagnosis was obtained in the last five years. Mean diagnostic delay was longer in Group B than in Group A (12,26 ± 13,32 vs. 5.66 ± 6.41 years). Chorea as an initial movement disorder was associated with a significantly longer diagnostic delay and a higher age at etiological diagnosis.ConclusionsThis study suggested: (a) a higher frequency of genetic defects involving neurotransmission, neuronal excitability, or neural development in patients with hyperkinetic movement disorders; (b) a higher frequency of neurodegenerative courses and a longer diagnostic delay in patients with hypokinetic movement disorders.

Evaluation of Embroidery Defects and Solution Proposals in Clothing Sector

Nowadays, it has become more important to follow the latest developments concerning the clothing sector as staying up to date is likely to help us eliminate unnecessary expenditures and increase productivity. In order for companies to continue and expand their activities in ever-changing market conditions compared to their competitors, they need to make their own enterprises compatible with this emerging technology as much as possible by following the developing technology. In this study, the embroidery department of an exporting company was examined. More specifically, the embroidered products were carefully checked and faulty pieces were separated and classified according to the types of defects. The most critical defects were determined by means of Pareto analysis according to defect types and frequencies and an auxiliary apparatus was placed on the embroidery machine to prevent the defect in question. After the placement of the apparatus, the products were checked again. As a result of the calculations, the percentage of the defects in question decreased from 42.41% to 4.6%, and an improvement of 37.81% was achieved with the apparatus developed within the scope of this study.

Adaptive MOMEDA model based variational mode decomposition for Pelton wheel fault detection

A critical step in fault diagnosis is determining the frequency of faults. Variational mode decomposition (VMD) is extensively employed for this purpose since it can describe the signal in the time-frequency domain. On the other hand, the VMD frequently fails to analyse non-stationary data containing low-frequency disturbances/noises. A multipoint optimal minimal entropy deconvolution adjusted (MOMEDA) is used with VMD in this research to improve defect detection performance in the presence of low-frequency disturbances. The filter length has a strong influence on the output of MOMEDA thus choosing the right one is a critical step in recovering a periodic pulse in the event of a weak defective signal. Improved grey wolf optimization (GWO) adaptively selects the appropriate filter length using the autocorrelation energy as its fitness function. The GWO is improved by introducing a gaussian mutation strategy which maintains the proper balance between the exploration and exploitation process. The proposed method has been applied to investigate the bucket defects of the Pelton wheel. The raw vibration signal is first decomposed into a series of modes using VMD. Second, the MOMEDA model is used to purify each mode by reducing low-frequency noise interference. The modes processed by MOMEDA are reconstructed again into a pure signal. Finally, the Hilbert envelop spectrum of the pure signal is obtained to determine the fault frequency. The same is verified from the theoretical fault feature frequency of the turbine bucket. The performance of the proposed method in extracting fault frequency accurately is also compared with other two models: (a) MOMEDA model with ensemble empirical mode decomposition (EEMD) and (b) MOMEDA model with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The comparison results proved the efficacy and superiority of the proposed method.

Inverse thresholding to spectrogram for the detection of broken rotor bar in induction motor

This paper introduces a reliable approach based on start-up transient analysis using inverse thresholding to spectrogram for early detection of a broken rotor bar fault in an induction motor. The investigation has been done in the time–frequency plane since it is based on the non-stationarity assessment of fault frequencies. Typically, time–frequency analysis is carried out in the presence of the fundamental, resulting in the fault features appearing unnoticed. The proposed technique addresses this issue by weakening the fundamental, making fault features more clearly apparent. To achieve this, inverse thresholding to spectrogram has been utilized for motor start-up current. In addition, a quantitative analysis of machines’ health has also been offered. A health score has been calculated based on the spectral entropy in the start-up current to quantify the condition of the motor. To examine the rotor fault, a Finite Element Model (FEM) of the machine is simulated in the ANSYS 2D workbench. The suggested method has been validated with simulated data in multiple simulation scenarios with varying load torques to demonstrate the method’s efficacy. Finally, the diagnosis method is tested against a publicly available data set related to the rotor bar fault.

A low-frequency fault detection method for low-speed planetary gearbox based on acoustic signals

In some practical application of construction machinery and equipment, the planetary gearbox in the walking parts is wrapped with tires, and its speed is very low. When this planetary gearbox fails, the fault frequency is very low (less than 1 Hz) due to its complex transmission relationship. Because the planetary gearbox has the characteristics of low speed, low fault frequency and surrounded by tires, it is difficult to detect its fault condition in practical engineering. This work proposes a new low-frequency fault detection method. First, the pre-processing is used for the measured acoustic signals. Second, the pre-processed acoustic signals are decomposed into components by the multi-resolution singular value decomposition. Third, the autoregressive model is constructed to select fault-related component. Finally, the output component is further analyzed by envelope spectrum analysis. The results indicate that the proposed method can accurately detect the characteristic information of low frequency faults of planetary gearbox in practical engineering.