Gearbox Deterioration Detection under Steady State,Variable Load, and Variable Speed Conditions

Multiple dominant gear meshing frequencies are present in the vibration signals collected from gearboxes and the conventional spiky features that represent initial gear fault conditions are usually difficult to detect. In order to solve this problem, we propose a new gearbox deterioration detection technique based on autoregressive modeling and hypothesis testing in this paper. A stationary autoregressive model was built by using a normal vibration signal from each shaft. The established autoregressive model was then applied to process fault signals from each shaft of a two-stage gearbox. What this paper investigated is a combined technique which unites a time-varying autoregressive model and a two sample Kolmogorov-Smimov goodness-of-fit test, to detect the deterioration of gearing system with simultaneously variable shaft speed and variable load. The time-varying autoregressive model residuals representing both healthy and faulty gear conditions were compared with the original healthy time-synchronous average signals. Compared with the traditional kurtosis statistic, this technique for gearbox deterioration detection has shown significant advantages in highlighting the presence of incipient gear fault in all different speed shafts involved in the meshing motion under variable conditions.

Mine-hoist fault-condition detection based on the wavelet packet transform and kernel PCA

A new algorithm was developed to correctly identify fault conditions and accurately monitor fault development in a mine hoist. The new method is based on the Wavelet Packet Transform (WPT) and kernel PCA (Kernel Principal Compo- nent Analysis, KPCA). For non-linear monitoring systems the key to fault detection is the extracting of main features. The wavelet packet transform is a novel technique of signal processing that possesses excellent characteristics of time-frequency localization. It is suitable for analysing time-varying or transient signals. KPCA maps the original input features into a higher dimension feature space through a non-linear mapping. The principal components are then found in the higher dimen- sion feature space. The KPCA transformation was applied to extracting the main nonlinear features from experimental fault feature data after wavelet packet transformation. The results show that the proposed method affords credible fault detection and identification.

Instrument for insulation conditions detection of roof insulator on high-speed-train

With the rapid development of high-speed-railway,environment around high voltage device on train roof becomes very complicated. Most train accidents happened due to occurrence of flashover on roof insulator,but the insulation condition estimation of insulator in such environment is much difficult. To ensure the insulation property of electric equipment,and guarantee the operation safety of high-speed-train,here established an instrument with high reliability which can on-line monitor insulation condition of roof insulator and give out advanced alarm before the incipient insulator flashover. The instrument consists of three parts,Data Acquisition & Sensor,Data Processing and Back Processing. Anti-interference and protection methods are processed to Rogowski coil sensor for better leakage current signal. To avoid the fluctuation from railway power supply,four modules are set to filter the power supply waveform. Through laboratory measurement,it is shown that the leakage current and the impedance angle can be detected by the instrument accurately. From the comparison of leakage current and impedance angle results under different moisture condition and the alarm operation when leakage current value reached threshold,this instrument can give out enough information for staff to understand the insulation condition of insulator.

A method of convolutional neural network based on frequency segmentation for monitoring the state of wind turbine blades

Wind turbine blades are prone to failure due to high tip speed,rain,dust and so on.A surface condition detecting approach based on wind turbine blade aerodynamic noise is proposed.On the experimental measurement data,variational mode decomposition filtering and Mel spectrogram drawing are conducted first.The Mel spectrogram is divided into two halves based on frequency characteristics and then sent into the convolutional neural network.Gaussian white noise is superimposed on the original signal and the output results are assessed based on score coefficients,considering the complexity of the real environment.The surfaces of Wind turbine blades are classified into four types:standard,attachments,polishing,and serrated trailing edge.The proposed method is evaluated and the detection accuracy in complicated background conditions is found to be 99.59%.In addition to support the differentiation of trained models,utilizing proper score coefficients also permit the screening of unknown types.

Remote condition monitoring of rail tracks using distributed acoustic sensing(DAS):A deep CNN-LSTM-SW based model

Railroad condition monitoring is paramount due to frequent passage through densely populated regions.This significance arises from the potential consequences of accidents such as train derailments,hazardous materials leaks,or collisions which may have far-reaching impacts on communities and the surrounding areas.As a solution to this issue,the use of distributed acoustic sensing(DAS)-fiber optic cables along railroads provides a feasible tool for monitoring the health of these extended infrastructures.Nevertheless,analyzing DAS data to assess railroad health or detect potential damage is a challenging task.Due to the large amount of data generated by DAS,as well as the unstructured patterns and substantial noise present,traditional analysis methods are ineffective in interpreting this data.This paper introduces a novel approach that harnesses the power of deep learning through a combination of CNNs and LSTMs,augmented by sliding window techniques(CNN-LSTM-SW),to advance the state-of-the-art in the railroad condition monitoring system.As well as it presents the potential for DAS and fiber optic sensing technologies to revolutionize the proposed CNN-LSTM-SW model to detect conditions along the rail track networks.Extracting insights from the data of High tonnage load(HTL)-a 4.16 km fiber optic and DAS setup,we were able to distinguish train position,normal condition,and abnormal conditions along the railroad.Notably,our investigation demonstrated that the proposed approaches could serve as efficient techniques for processing DAS signals and detecting the condition of railroad infrastructures at any remote distance with DAS-Fiber optic cable setup.Moreover,in terms of pinpointing the train's position,the CNN-LSTM architecture showcased an impressive 97%detection rate.Applying a sliding window,the CNN-LSTM labeled data,the remaining 3%of misclassified labels have been improved dramatically by predicting the exact locations of each type of condition.Altogether,these proposed models exhibit promising potential for accurately identifying various railroad conditions,including anomalies and discrepancies that warrant thorough exploration.

A Novel and Reliable Spectrophotometric Method for Determination of Lactoperoxidase Activity in Raw Milk

With 3, 3’5, 5’-tetramethylbenzidine（TMB） as the detection substrate, a reliable and highly selective method was established and optimized for the determination of Lactoperoxidase（LP） activity in raw milk. The method was based on the enzymatic reaction principle, where hydrogen peroxide oxidated TMB in the presence of LP. The optimized conditions of this assay system were obtained, consisting of 20 mmol · L-1 TMB solution, 0.6 mmol · L-1 hydrogen peroxide and 0.1 mol · L-1 Citric Acid（CA）/0.2 mol · L-1 disodium hydrogen phosphate（Na P） buffer（pH 4.8）. TMB detection method was applied to the analysis of LP in milk samples with a practical working concentration range from 2 to 14 mg · L-1. The intra-and inter-batch variation coefficients were all below 5%, indicating a good repeatability. Confirmation test between TMB method and 2, 2-azinobi（3-ethylbenzothiazoline-6-sulphonate） diammonium salt（ABTS） method was carried out, and the results of TMB assay were in accordance with that of ABTS method.

Road surface condition sensor based on scanning detection of backward power

A method of detecting dry, icy and wet road surface conditions based on scanniag detection of single wavelength backward power is proposed in this letter. The detector is used to receive the backward scattered power which changes with the incidence angle. The relationship between backward power and incidence angle is used to find out the effective angle range and distinguish method. Experiment and simulation show that it is feasible to classifv these three conditions within incidence angle of 5.3 degree.

Early detection of rotating stallin axial flow compressors via deterministic learning:detectability analysis

Rotating stall and surge are two violent unstable phenomena of an aero-engine compressor.The early detection of rotating stall is a critical and difficult issue in the operation of a compressor.Recently,a deterministic learning based stall inception detection approach(SIDA)has been developed for modeling and detecting stall inception in aero-engine compressors.This paper considers the derivation of analytical results on the detection capabilities for the SIDA based on deterministic learning.First,by utilizing the input/output stability of the residual system,a detectability condition of the SIDA is presented,and how to choose the parameters of the diagnostic system is also analyzed.Second,based on the relationship between NN approximation capabilities and radial basis function(RBF)network structures,the influence of RBF network structures on the performance properties of the SIDA is analyzed.Finally,a simulation study is presented,in which the Mansoux-C2 compressor model is utilized to verify the effectiveness of the proposed SIDA.