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This paper aims to explore a new wavelet adaptive threshold de-noising method to resolve the shortcomings of wavelet hard-threshold method and wavelet soft-threshold method, which are usually used in gear fault diagnosis.

A new threshold function and a new determined method of threshold for each layer are proposed. The principle and the implementation of the algorithm are given. The simulated signal and the measured gear fault signal are analyzed, and the obtained results are compared with those from wavelet soft-threshold method, wavelet hard-threshold method and wavelet modulus maximum method.

The presented wavelet adaptive threshold method overcomes the defects of the traditional wavelet threshold method, and it can effectively eliminate the noise hidden in the gear fault signal at different decomposition scales. It provides more accurate information for the further fault diagnosis.

A new threshold function is adopted and the multi-resolution unbiased risk estimation is used to determine the adaptive threshold, which overcomes the defect of the traditional wavelet method.

Noise abatement is one of the key techniques for Partial Discharge (PD) on-line measurement and monitoring. However, how to enhance the efficiency of PD signal noise suppression is a challenging work. Hence, this study aims to improve the efficiency of PD signal noise abatement.

In this approach, the time–frequency characteristics of PD signal had been obtained based on fast kurtogram and S-transform time–frequency spectrum, and these characteristics were used to optimize the parameters for the signal matching over-complete dictionary. Subsequently, a self-adaptive selection of matching atoms was realized when using Matching Pursuit (MP) to analyze PD signals, which leading to seldom noise signal element was represented in sparse decomposition.

The de-noising of PD signals was achieved efficiently. Simulation and experimental results show that the proposed method has good adaptability and significant noise abatement effect compared with Empirical Mode Decomposition, Wavelet Threshold and global signal sparse decomposition of MP.

A self-adaptive noise abatement method was proposed to improve the efficiency of PD signal noise suppression based on the signal sparse representation and its MP algorithm, which is significant to on-line PD measurement.

Recently, the convolutional neural network (ConvNet) has a wide application in the classification of motor imagery EEG signals. However, the low signal-to-noise electroencephalogram (EEG) signals are collected under the interference of noises. However, the conventional ConvNet model cannot directly solve this problem. This study aims to discuss the aforementioned issues.

To solve this problem, this paper adopted a novel residual shrinkage block (RSB) to construct the ConvNet model (RSBConvNet). During the feature extraction from EEG signals, the proposed RSBConvNet prevented the noise component in EEG signals, and improved the classification accuracy of motor imagery. In the construction of RSBConvNet, the author applied the soft thresholding strategy to prevent the non-related motor imagery features in EEG signals. The soft thresholding was inserted into the residual block (RB), and the suitable threshold for the current EEG signals distribution can be learned by minimizing the loss function. Therefore, during the feature extraction of motor imagery, the proposed RSBConvNet de-noised the EEG signals and improved the discriminative of classification features.

Comparative experiments and ablation studies were done on two public benchmark datasets. Compared with conventional ConvNet models, the proposed RSBConvNet model has obvious improvements in motor imagery classification accuracy and Kappa coefficient. Ablation studies have also shown the de-noised abilities of the RSBConvNet model. Moreover, different parameters and computational methods of the RSBConvNet model have been tested on the classification of motor imagery.

Based on the experimental results, the RSBConvNet constructed in this paper has an excellent recognition accuracy of MI-BCI, which can be used for further applications for the online MI-BCI.

The objective of this paper is to provide a brief review of recent developments in the area of applications of ANN, Fuzzy Logic, and Wavelet Transform in fault diagnosis. The purpose of this work is to provide an approach for maintenance engineers for online fault diagnosis through the development of a machine condition‐monitoring system.

A detailed review of previous work carried out by several researchers and maintenance engineers in the area of machine‐fault signature‐analysis is performed. A hybrid expert system is developed using ANN, Fuzzy Logic and Wavelet Transform. A Knowledge Base (KB) is created with the help of fuzzy membership function. The triangular membership function is used for the generation of the knowledge base. The fuzzy‐BP approach is used successfully by using LR‐type fuzzy numbers of wavelet‐packet decomposition features.

The development of a hybrid system, with the use of LR‐type fuzzy numbers, ANN, Wavelets decomposition, and fuzzy logic is found. Results show that this approach can successfully diagnose the bearing condition and that accuracy is good compared with conventionally EBPNN‐based fault diagnosis.

The work presents a laboratory investigation carried out through an experimental set‐up for the study of mechanical faults, mainly related to the rolling element bearings.

The main contribution of the work has been the development of an expert system, which identifies the fault accurately online. The approaches can now be extended to the development of a fault diagnostics system for other mechanical faults such as gear fault, coupling fault, misalignment, looseness, and unbalance, etc.

In real working condition, signal is highly disturbed and even drowned by noise, which extremely interferes in detecting results. Therefore, this paper aims to provide an effective de-noising method for the debris particle in lubricant so that the ultrasonic technique can be applied to the online debris particle detection.

For completing the online ultrasonic monitoring of oil wear debris, the research is made on some selected wear debris signals. It applies morphology component analysis (MCA) theory to de-noise signals. To overcome the potential weakness of MCA threshold process, it proposes fuzzy morphology component analysis (FMCA) by fuzzy threshold function.

According to simulated and experimental results, it eliminates most of the wear debris signal noises by using FMCA through the signal comparison. According to the comparison of simulation evaluation index, it has highest signal noise ratio, smallest root mean square error and largest similarity factor.

The rapid movement of the debris particles, as well as the lubricant temperature, may influence the measuring signals. Researchers are encouraged to solve these problems further.

This paper includes implications for the improvement in the online debris detection and the development of the ultrasonic technique applied in online debris detection.

This paper provides a promising way of applying the MCA theory to de-noise signals. To avoid the potential weakness of the MCA threshold process, it proposes FMCA through fuzzy threshold function. The FMCA method has great obvious advantage in de-noising wear debris signals. It lays the foundation for online ultrasonic monitoring of lubrication wear debris.

Measurement-while-drilling (MWD) system has been used to provide trajectory and inclination parameters of the oil and gas well. Fluxgate magnetometer is a traditional choice for one MWD system; however, it cannot obtain effective trajectory parameters in nonmagnetic environments. Fiber-optic-gyroscope (FOG) inclinometer system is a favorable substitute of fluxgate magnetometer, which can avoid the flaws associated with magnetic monitoring devices. However, there are some limitations and increasing surveying errors in this system under high impact conditions. This paper aims to overcome these imperfections of the FOG inclinometer system.

To overcome the imperfections, filtering algorithms are used to improve the precision of the equipment. The authors compare the low-pass filtering algorithm with the wavelet de-noising algorithm applied to real experimental data. Quantitative comparison of the error between the true and processed signal revealed that the wavelet de-noising method outperformed the low-pass filtering method. To achieve optimal positioning effects, the wavelet de-noising algorithm is finally used to inhibit the interference caused by the impact.

The experimental results show that the method proposed can ensure the azimuth accuracy lower than ±2 degrees and the inclination accuracy lower than ± 0.15 degrees under the condition of interval impact. The method proposed can overcome the interference generated by the impact in the well, which makes the instrument suitable for the measurement of small-diameter casing well.

After conducting the wavelet threshold filtering on the raw data of accelerometers, the noise generated by the impact is successfully suppressed, which is expected to meet the special requirement of the down-hole survey environment.

Denoising of the vibration signal is crucial to identify a structure's damage. Based on noise frequency character, the “real” vibration signal can be gotten. The purpose of this paper is to propose a novel method for denoising a signal based on the wavelet transform.

The vibration signal with noise which can be collected by wireless network is decomposed by wavelet transform. In order to select optimal level of wavelet decomposition, based on noise's frequency, power spectral density is used. A soft thresholding method based on minimum mean‐variance is used for vibration signal de‐noising with Gaussian noise.

A novel method has been described in his paper. Based on the relationship between vibration signal's character and noise frequency, the way to get rid of noise is combined wavelet transform with power spectral density.

In order to select optimal level of wavelet decomposition, based on noise's frequency, power spectral density is used. A soft thresholding method based on minimum mean‐variance is used for vibration signal denoising with Gaussian noise.

The purpose of this paper is to study the relationship between arc sound signal and arc height through arc sound features of GTAW welding, which is aimed at laying foundation work for monitoring the welding penetration and quality by using the arc sound signal in the future.

The experiment system is based on GTAW welding with acoustic sensor and signal conditioner on it. The arc sound signal was first processed by wavelet analysis and wavelet packet analysis designed in this research. Then the features of arc sound signal were extracted in time domain, frequency domain, for example, short‐term energy, AMDF, mean strength, log energy, dynamic variation intensity, short‐term zero rate and the frequency features of DCT coefficient, also the wavelet packet coefficient. Finally, a ANN (artificial neural networks) prediction model was built up to recognize different arc height through arc sound signal.

The statistic features and DCT coefficient can be absolutely used in arc sound signal processing; and these features of arc sound signal can accurately react the modification of arc height during the GTAW welding process.

This paper tries to make a foundation work to achieve monitoring arc length through arc sound signal. A new way to remove high frequency noise of arc sound signal is produced. It proposes some effective statistic features and a new way of frequency analysis to build the prediction model.

The purpose of this paper is to employ the acoustic wave propagation method for leakage detection in pipes. The first objective is to use acoustic finite element analysis (AFEA) method to simulate acoustic wave propagation and acoustic wave reflectometry in an intact pipe and in pipes with leaks of various sizes. This is followed by the second objective which is to validate the effectiveness and the practicability of the acoustic wave method via experimental testing. The third objective involves the decomposition and de-noising of the measured acoustic waves using stationary wavelet transform (SWT). It is shown that this approach, which is used for the first time on leakage detection in pipes, can be used to identify, locate and estimate the size of a leakage defect in a pipe.

The research work was designed inline with best practices and acceptable standards. The research methodology focusses on five basic areas: literature review; experimental measurements; simulations; data analysis and writing-up of the study with clear-cut communication of the findings. The approach used was acoustic wave propagation-based method in conjunction with SWT for leakage detection in fluid-filled pipe.

First, the simulation of acoustic wave propagation and acoustic wave reflectometry in fluid-filled pipes with and without leakage have great potential in leakage detection in pipeline systems and can detect very small leaks of 1 mm diameter. Second, the measured noise-contaminated acoustic wave propagation in a fluid-filled pipe can be successfully de-noised using the SWT method in order to clearly identify and locate leakage as little as 5 mm diameter in a pipe. Third, AFEA of a fluid-filled pipe can be achieved with the simulation of only the fluid content of the pipe and without the inclusion of the pipe in the model. This eliminates contact interaction of the solid pipe walls and the fluid, and as a consequence reduces computational time and resources. Fourth, the relationship of the ratio of the leakage diameter to the ratio of the first and second secondary wave amplitudes caused by the leakage can be represented by a second-order polynomial function. Fifth, the identification of leakage in a pipe is intuitive from mere comparison of the acoustic waveforms of an intact pipe with that of a pipe with a leakage.

The research work is a novelty and was developed from the scratch. The AFEA of acoustic wave propagation and acoustic wave reflectometry in a static fluid-filled pipe, and the SWT method have been used for the first time to detect, locate and estimate the size of a leakage in a fluid-filled pipe.

The purpose of this study is to analyze the corrosion behavior of 304SS in three kinds of solution, 3.5 per cent NaCl, 5 per cent H₂SO₄ and 1 M (1 mol/L) NaOH, using electrochemical noise.

Corrosion types and rates were characterized by spectrum and time-domain analysis. EN signals were evaluated using a novel method of phase space reconstruction and chaos theory. To evaluate the chaotic characteristics of corrosion systems, the delay time was obtained by the mutual information method and the embedding dimension was obtained by the average false neighbors method.

The varying degrees of chaos in the corrosion systems were indicated by positive largest Lyapunov exponents of the electrochemical potential noise.

The change of correlation dimension in three kinds of solution demonstrated significant differences, clearly differentiating various types of corrosion.