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This paper aims to solve the problem that multiscale dispersion entropy (MDE) is prone to information loss in the process of coarse-grain, which makes it difficult to extract bearing fault information comprehensively.

A new fault diagnosis method of rolling bearing using refined composite multiscale peak-to-peak normalized dispersion entropy (RCMPNDE) and sparrow search algorithm optimized probabilistic neural network (SSA-PNN) is proposed. First, coarse-graining employs the peak-to-peak value calculation instead of the segmented mean calculation in the RCMDE algorithm, which can overcome the shortcomings of traditional coarse-graining and highlight the fault characteristics. Then, the influence of the selection of different parameters is reduced through the normalization operation, and the RCMPNDE is formed. Finally, the extracted feature parameters are combined with SSA-PNN for diagnosis recognition to construct the RCMPNDE-SSA-PNN fault diagnosis method.

The proposed RCMPNDE-SSA-PNN fault diagnosis method is tested on actual data sets and its outcomes have been compared to those generated by methods built upon MDE, RCMDE and PNN. The comparison results showed that the proposed method can extract the fault feature information of rolling bearings more accurately and improve the accuracy of fault classification. The recognition accuracy reached 98.5% under the conditions of this experiment.

The RCMPNDE-SSA-PNN method can obtain more accurate fault diagnosis accuracy and provide a new reliable diagnosis method for rolling bearing fault diagnosis.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2024-0332/

The primary objective of this research is to explore the impact of the repeated two-syllable communication strategy on the interaction effectiveness between AI and customers.

This study adopts an experimental research methodology to investigate the role of the repeated two-syllable communication strategy employed by AI customer service agents.

Study 1 shows that AI agents using the repeated two-syllable strategy enhance the interaction effectiveness between AI and customers. Study 2 identifies humanization perception as a key factor linking the strategy to better interaction effectiveness. Study 3 highlights how consumer materialism moderates this effect, while Study 4 examines how the type of agent (AI vs. human) influences the results.

This study extends the application of AI communication strategies in interactive marketing, specifically how AI agents enhance consumer interaction through repeated two-syllable communication. It pioneers the exploration of AI-human interaction, enriching the humanization theory by revealing how AI can evoke emotional responses. The study also integrates consumer materialism as a moderating factor, offering new theoretical and practical insights for brands to optimize AI-customer service interactions and improve engagement in real-world marketing contexts.