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Elevators, escalators and travelators have been developed to enable people to save time and physical strength when traveling. As places such as shopping malls, hotels and airports have become dependent on these transportation technologies, major problems can occur when they stop working. This paper reviews the historical steps used to address the reliability and safety of these human transportations – technologies and how fault diagnosis, fault detection and health monitoring, and maintenance forecasting are now being implemented, as well as the potential for further advancements.

Eddy current testing (ECT) is an established non-destructive evaluation (NDE) technique to evaluate materials. In last decade, machine learning (ML) has revolutionized many areas and ECT is not an exception. The focus of ML in ECT system is to automate some of its analyses for the possible in-situ monitoring of the process and to alleviate the interpretation burden on the operator. The fusion of ML and ECT is not new, however, due to recent advancements in machine learning, there is a need to assess the current potential of ML for ECT systems and identify any gaps and shortcomings for automated data analysis. Thus, this article discusses the findings of a literature survey about the contemporary methods of using machine learning for the automatic analysis of ECT data. The application of machine learning for the ECT system is described in a general workflow manner that begins with data collection and ends with the verification and validation of the performance of ML models. Findings on potential areas of application of the fusion of ML and ECT along with limitations and potential gaps are discussed. This study also identifies the need for common datasets, sample size determination and uncertainty quantification of ML models.

Due to the complexity of the aging process, maintaining the state of health (SOH) of lithium-ion batteries is a significant challenge that must be overcome. This study presents a new SOH estimation approach based on hybrid Grey Wolf Optimization (GWO) with Bayesian Regularized Neural Networks (BRNN). The approach utilizes health features (HFs) extracted from the battery charging-discharging process. Selected external voltage and current characteristics from the charging-discharging process serve as HFs to explain the aging mechanism of the batteries. The Pearson correlation coefficient, the Kendall rank correlation coefficient, and the Spearman rank correlation coefficient are then employed to select HFs that have a high degree of association with battery capacity. In this paper, GWO is introduced as a method for optimizing and selecting appropriate hyper-p parameters for BRNN. GWO-BRNN updates the population through mutation, crossover, and screening operations to obtain the globally optimal solution and improve the ability to conduct global searches. The validity of the proposed technique was assessed by examining the NASA battery dataset. Based on the simulation results, the presented approach demonstrates a higher level of accuracy. The proposed GWO-BRNN-based SOH estimation achieves estimate assessment indicators of less than 1%, significantly lower than the estimated results obtained by existing approaches. The proposed framework helps develop electric vehicle battery prognostics and health management for the widespread use of eco-friendly and reliable electric transportation.

In this letter, we investigate a chaotic synchronization-based secure communication system at radio frequency (RF). In the system, the information signal, which is encrypted in the baseband by the use of a chaotic system, is modulated and transmitted in the RF band. At the receiver, the encrypted signal is demodulated and then decrypted by the use of chaotic synchronization. Experiments conducted at both baseband and RF verify our theoretical analysis, and prove the feasibility of our proposed communication system. © 2000 John Wiley & Sons, Inc. Microwave Opt Technol Lett 26: 407–410, 2000. [ABSTRACT FROM AUTHOR]