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This paper aims to analyze the characteristics of friction vibration signals and identify the vibration excitation source at the start and stop stage of microtextured end face of dry gas seals.

The friction pair consists of a diamond-like carbon (DLC) film microtextured seal ring and a spiral groove seal ring. Friction vibration signal feature extraction method based on harmonic wavelet packet and spectrum analysis was proposed. Signals were collected using acceleration sensor, acquisition card and LabVIEW software. Vibration acceleration signal was decomposed into 32 frequency bands using MATLAB wavelet packet transformation. The 32nd band coefficient was extracted for reconstruction, time-domain and spectral waveforms were obtained and spectra before/after denoising were compared.

The end face of the DLC film microtextured seal ring generates a good dynamic pressure effect, and the friction and vibration reduction effects are obvious. The harmonic wavelet packet can decompose the vibration signal conveniently and precisely. In the case of this experiment, the frequency of vibration of the seal ring is 7500 HZ.

The results show that the method is effective for the processing of friction vibration signal and the identification of vibration excitation source. The findings will provide ideas for the frictional vibration signal processing and basis for further research in the field of tribology of dry gas seal ring.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0084/

This study aims to develop and test a framework of the antecedents to and performance outcomes of exporters’ use of different services offered by Internet-based Business-to-Business (I-B2B) platforms.

We test the model based on a unique survey dataset of 350 Chinese exporters who subscribed to Alibaba.com, a major I-B2B platform.

Drawing on the signaling theory, export and I-B2B platform literature, we develop a typology of exporters’ use of services offered by I-B2B platforms. We find that the extent to which firms have cost efficiency advantages, adopt an export diversity strategy, operate under high levels of psychic distance and experience high levels of domestic regulatory uncertainty are all positively related to exporters’ use of I-B2B platform services. The use of those services is either positively or negatively related to export success depending on the services in question. The magnitudes of these performance relationships are contingent on the exporters’ transparency strategies.

This is the first study to examine the antecedents to and consequences of exporters’ use of the services offered by I-B2B platforms.

The safe operation of the metro power transformer directly relates to the safety and efficiency of the entire metro system. Through voiceprint technology, the sounds emitted by the transformer can be monitored in real-time, thereby achieving real-time monitoring of the transformer’s operational status. However, the environment surrounding power transformers is filled with various interfering sounds that intertwine with both the normal operational voiceprints and faulty voiceprints of the transformer, severely impacting the accuracy and reliability of voiceprint identification. Therefore, effective preprocessing steps are required to identify and separate the sound signals of transformer operation, which is a prerequisite for subsequent analysis.

This paper proposes an Adaptive Threshold Repeating Pattern Extraction Technique (REPET) algorithm to separate and denoise the transformer operation sound signals. By analyzing the Short-Time Fourier Transform (STFT) amplitude spectrum, the algorithm identifies and utilizes the repeating periodic structures within the signal to automatically adjust the threshold, effectively distinguishing and extracting stable background signals from transient foreground events. The REPET algorithm first calculates the autocorrelation matrix of the signal to determine the repeating period, then constructs a repeating segment model. Through comparison with the amplitude spectrum of the original signal, repeating patterns are extracted and a soft time-frequency mask is generated.

After adaptive thresholding processing, the target signal is separated. Experiments conducted on mixed sounds to separate background sounds from foreground sounds using this algorithm and comparing the results with those obtained using the FastICA algorithm demonstrate that the Adaptive Threshold REPET method achieves good separation effects.

A REPET method with adaptive threshold is proposed, which adopts the dynamic threshold adjustment mechanism, adaptively calculates the threshold for blind source separation and improves the adaptability and robustness of the algorithm to the statistical characteristics of the signal. It also lays the foundation for transformer fault detection based on acoustic fingerprinting.

Ciliated microelectromechanical system (MEMS) vector hydrophones pick up sound signals through Wheatstone bridge in cross beam-ciliated microstructures to achieve information transmission. This paper aims to overcome the complexity and variability of the marine environment and achieve accurate location of targets. In this paper, a new method for ocean noise denoising based on improved complete ensemble empirical mode decomposition with adaptive noise combined with wavelet threshold processing method (CEEMDAN-WT) is proposed.

Based on the CEEMDAN-WT method, the signal is decomposed into different intrinsic mode functions (IMFs), and relevant parameters are selected to obtain IMF denoised signals through WT method for the noisy mode components with low sample entropy. The final pure signal is obtained by reconstructing the unprocessed mode components and the denoising component, effectively separating the signal from the wave interference.

The three methods of empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and CEEMDAN are compared and analyzed by simulation. The simulation results show that the CEEMDAN method has higher signal-to-noise ratio and smaller reconstruction error than EMD and EEMD. The feasibility and practicability of the combined denoising method are verified by indoor and outdoor experiments, and the underwater acoustic experiment data after processing are combined beams. The problem of blurry left and right sides is solved, and the high precision orientation of the target is realized.

This algorithm provides a theoretical basis for MEMS hydrophones to achieve accurate target positioning in the ocean, and can be applied to the hardware design of sonobuoys, which is widely used in various underwater acoustic work.

This study aims to propose a method for monitoring bearing health in the time–frequency domain, termed the Lock-in spectrum, to track the evolution of bearing faults over time and frequency.

The Lock-in spectrum uses vibration signals captured by vibration sensors and uses a lock-in process to analyze specified frequency bands. It calculates the distribution of signal amplitudes around fault characteristic frequencies over short time intervals.

Experimental results demonstrate that the Lock-in spectrum effectively captures the degradation process of bearings from fault inception to complete failure. It provides time-varying information on fault frequencies and amplitudes, enabling early detection of fault growth, even in the initial stages when fault signals are weak. Compared to the benchmark short-time Fourier transform method, the Lock-in spectrum exhibits superior expressive ability, allowing for higher-resolution, long-term monitoring of bearing condition.

The proposed Lock-in spectrum offers a novel approach to bearing health monitoring by capturing the dynamic evolution of fault frequencies over time. It surpasses traditional methods by providing enhanced frequency resolution and early fault detection capabilities.

The call for supply chain transparency (SCT), especially the environmental, social and governance (ESG) aspect, is getting increasingly louder. Based on the signaling theory, our study investigates the operational benefit of supply chain transparency in terms of ESG (SCT-ESG). To further clarify the signaling process, the moderating roles of digitalization of the firm and signal strength are also examined.

Longitudinal secondary data from multiple databases are matched and analyzed using ordinary least squares (OLS) regressions to validate the proposed hypotheses.

Results suggest that with SCT-ESG, firms have a weakened disparity between production variance and demand variance, and the supply chain experiences a reduced bullwhip effect. Further, digitalization of the focal company and signal strength reinforce the negative effect of SCT-ESG on the bullwhip effect.

The study integrates the SCT and ESG literature through SCT-ESG, extending benefits of ESG disclosure to the supply chain context. It extends the application of the signaling theory in OSCM by including contextual factors of digitalization and signal strength.

The purpose of this paper is to use the corresponding magnetic sensor and detection method to detect and image the defects of small diameter pipelines. Urban gas pipeline is an energy transportation tool for urban industrial production and social life, which is closely related to urban safety. Preventing the occurrence of urban gas pipeline transportation accidents and carrying out pipeline defect detection are of great significance for the urban economic and social stability. To perform pipeline defect detection, the magnetic flux leakage internal detection method is generally used in the detection of large-diameter long-distance oil and gas pipelines. However, in terms of the internal detection of small-diameter pipelines, due to the heavy weight, large structure of the detection device and small pipe diameter, the detection is more difficult.

In order to solve the above matters, self-made three-dimensional magnetic sensor and three-dimensional magnetic flux leakage imaging direct method are proposed for studying the defect identification. Firstly, for adapting to the diameter range of small-diameter pipelines, and containing the complete information of the defect, a self-made three-dimensional magnetic sensor is made in this paper to improve the accuracy of magnetic flux leakage detection. And on the basis of it, a small diameter pipeline defect detection system is built. Secondly, as detection signal may be affected by background magnetic field interference and the jitter interference, the complete ensemble empirical mode decomposition with adaptive noise method is utilized to screen the detected signal. As a result, the useful signal is reconstructed and the interference signal is removed. Finally, the defect contour inversion imaging of detection is realized based on the direct method of three-dimensional magnetic flux leakage imaging, which includes three-dimensional magnetic flux leakage detection data and data segmentation recognition.

The three-dimensional magnetic flux leakage imaging experimental results shown that, compared to the actual defects, the typical defects, irregular defects and crack groove defects can be analyzed by the magnetic flux leakage defect contour imaging method in qualitative and quantitative way respectively, which provides a new idea for the research of defect recognition.

A three-dimensional magnetic sensor is made to adapt the diameter range of small diameter pipeline, and based on it, a small-diameter pipeline defect detection system is built to collect and display the magnetic flux leakage signal.

In online exchange platforms' sponsored search advertising, the array of product quality signals within a keyword search results list plays a crucial role in shaping buyers' purchasing decisions. This research seeks to explore the impact of various quality signals – namely, ranking position, seller reputation and product price – on ad clicks. Additionally, it examines the role of keyword attributes, such as specificity and popularity, in modulating the effects of these quality signals on advertising clicks.

A total of 5,763 effective data points were collected from a leading B2B electronic platform company, and we employed negative binomial regression with Heckman correction methods to test the hypotheses.

The results indicate that in online exchange platforms, search ad clicks are significantly and positively affected by displayed signals such as ranking position, seller reputation and product price information. Notably, a U-shaped relationship emerges between product price and ad clicks. Furthermore, keyword specificity and popularity distinctly moderate the impact of these displayed signals on ad clicks within online exchange platforms.

This paper addresses the gap in existing research on search advertising by methodically analyzing the impact of various signals displayed in search results and how keyword attributes moderate ad clicks, all through a signaling theory lens.

Based on signaling theory, this paper aims to explore the impact of supply chain transparency (SCT) on firms' bank loan (BL) and supply chain financing (SCF) in the context of voluntary disclosure of supplier and customer lists.

Based on panel data collected from Chinese-listed firms between 2012 and 2021, fixed-effect models and a series of robustness checks are used to test the predictions.

First, improving SCT by disclosing major suppliers and customers promotes BL but inhibits SCF. Specifically, customer transparency (CT) is more influential in SCF than supplier transparency (ST). Second, supplier concentration (SC) weakens SCT’s positive impact on BL while reducing its negative impact on SCF. Third, customer concentration (CC) strengthens the positive impact of SCT on BL but intensifies its negative impact on SCF. Last, these findings are basically more pronounced in highly competitive industries.

This study contributes to the SCT literature by investigating the under-explored practice of supply chain list disclosure and revealing its dual impact on firms' access to financing offerings (i.e. BL and SCF) based on signaling theory. Additionally, it expands the understanding of the boundary conditions affecting the relationship between SCT and firm financing, focusing on supply chain concentration. Moreover, it advances signaling theory by exploring how financing providers interpret the SCT signal and enriches the understanding of BL and SCF antecedents from a supply chain perspective.

The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).

In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.

Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.

In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.