A method for computation of surface roughness of digital elevation model terrains via multiscale analysis

In this paper, an algorithm to compute surface roughness of digital elevation model (DEM) terrains via multiscale analysis is proposed. The algorithm employs the lifting scheme to generate multiscale DEMs. At each scale, the areas of pixels that are modified are computed. Granulometric analysis is employed to compute the average area of curvature regions in the terrain, and the average roughness of the terrain due the distribution of curvature regions. The selected case studies of the algorithm implementation demonstrated that the proposed algorithm provides a surface roughness parameter that is realistic with respect to the amplitudes and frequencies of the terrain, invariant with respect to rotation and translation, and has intuitive meaning. The algorithm allows for a good quantification of a region’s convexity/concavity over varying scales, distinguishing between shallow and deep incisions of valleys and ridges of the terrain, and hence, provides an accurate surface roughness parameter.     

An on-line condition monitoring system for induction motors via instantaneous power analysis

Condition monitoring is an important factor in assuring well-being of motors. Existing approaches of condition monitoring are dependent on expensive special sensors. This paper reviews various forms of existing condition monitoring methods and highlights the need for an economical intelligent fault diagnosis system. In this study, the methodology taken in developing a condition monitoring system for the motor bearing faults identification, utilizing the commonly available motor stator current and voltage is demonstrated. This unique diagnostic condition monitoring system provides continuous real time tracking of the various bearing defects and determines the severity which can be adopted for fast decision making. The study on different bearing faults under no-load and full-load conditions is carried out experimentally and analyzed, and the results on the real hardware implementation confirm the effectiveness of the proposed approach.     

An Online Fault Diagnosis System for Induction Motors via Instantaneous Power Analysis

This article aims to provide a new noninvasive method for the online diagnosis of bearing-localized faults under various loading conditions of the induction motors via instantaneous power analysis. The instantaneous noise variations and sensor offsets are considered to be one of the common factors that yield erroneous fault tracking in an online condition monitoring and fault diagnosis system. An adaptive threshold scheme has been designed to tackle the sensor offsets and instantaneous noise variations for reliable decision making on the existence of fault signatures in an arbitrary environment conditions. The performance of the designed threshold scheme has been evaluated on a motor with various bearing defects operating under various loading conditions. Detailed theoretical and experimental evaluations of several bearing-localized faults are presented. The results indicate the viability and effectiveness of the proposed method.