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Nowadays, mobile applications (Apps) have become a main form of mobile Internet services, and related applications in the geological disaster monitoring domain must follow this development trend. In this study, an innovative remote and intelligent landslide monitoring system was designed and developed, which can capture the in-depth sliding force state of the slope in real time. When it reaches the early warning threshold, the system immediately transmits the warning information to user terminals and warns users to initiate corresponding risk-avoidance plans. Next, using the developed system, an App of early warning information publishing program was developed to transmit the acquired sliding-force data by field monitoring devices to servers via Beidou Satellite or GPRS base station. The App can inquire background servers via WiFi or 4G for acquiring the monitoring data and curves of the side slope. Finally, the developed system was applied for the monitoring of the sliding mass in Zhoujiawan, Badong County, the Three Gorges Reservoir Region. The monitoring personnel could locate and inspect the failure characteristics of the deformation region in a timely manner using the developed App. The App data exhibited significant correlation and consistency with the monitored results, thus enhancing the inspection efficiency and allowing an effective emergency response.

This paper proposes an intelligent diagnosis method for gearbox using local mean decomposition and discrete hidden Markov models, including local mean decomposition, the energy difference spectrum of singular value, multiscale sample entropy, and the discrete hidden Markov model. How to extract feature information effectively and identify the fault type is key to making a diagnosis in the presence of strong noise. Combined with the Kurtosis criterion and correlation coefficient, the product function that contains the main characteristic frequency is filtered out by local mean decomposition. Next, the filtered local mean decompositions are used to construct the Hankel matrix and complete singular value decomposition. The denoised and reconstructed signals are achieved by an energy difference spectrum of singular value. Furthermore, the feature information after denoising is extracted by multiscale sample entropy. After combining the discrete hidden Markov models, the mechanical condition is identified. Practical examples of diagnoses for four gear types used in the gearbox can accurately identify the gear types, and the recognition rates of the various types are above 92%. The experiments shown here verify the effectiveness of the method proposed in this paper.

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