Early fault diagnosis and condition monitoring can reduce the consequential damage and breakdown maintenance, prolong the machine life and increase the performance of industrial systems. This paper describes a real fault detection problem of a high-power (3.2 MW) induction motor driving pumps in a heating plant. Steady-state vibration signals were collected and some characteristic low- and high- frequency features were observed, resulting in broken rotor bar diagnosis. The obtained results were verified by disassembling the motor, which proved that this particular technique can be successfully applied in induction motor fault detection.

An adaptive algorithm solving the on-line parameter estimation problem for a broad class of linear systems is proposed. The approach can be applied to systems with delay, distributed-parameter systems, fractional order systems, and others which are stable or stabilized by linear feedback. The proposed scheme can be applied to simultaneously track sufficiently slow changes in process gains, delays, time-constants, diffusivity and other parameters. The proposed method is gradient-based and it yields a relatively efficient numerical implementation. Convergence and robustness of the algorithm are investigated through Lyapunov analysis, yielding explicit convergence conditions which generalize the well-known “Persistence of Excitation” (P. E.) and identifiability requirements arising in conventional adaptive estimation. The method is illustrated by several examples.

Background/Objectives: The number of individuals with lower limb loss (LLL) is rising. Therefore, identifying the walking potential in individuals with LLL and prescribing adequate prosthetic systems are crucial. Various factors can influence participants’ walking ability, to different extents. The aim of the present study was to apply machine learning methods to develop a predictive mode. This model can assist rehabilitation and limb loss care teams in making informed decisions regarding prosthesis prescription and predicting walking ability in individuals with LLL. Methods: The present study was designed as a prospective cross-sectional study encompassing 104 consecutively recruited participants with LLL (average age 62.1 ± 10.9 years, 80 (76.9%) men) at the Medical Rehabilitation Clinic. Demographic, physical, psychological, and social status data of patients were collected at the beginning of the rehabilitation program. At the end of the treatment, K-level estimation of functional ability, a Timed Up and Go Test (TUG), and a Two-Minute Walking Test (TMWT) were performed. Support vector machines (SVM) were used to develop the prediction model. Results: Three decision trees were created, one for each output, as follows: K-level, TUG, and TMWT. For all three outputs, there were eight significant predictors (balance, body mass index, age, Beck depression inventory, amputation level, muscle strength of the residual extremity hip extensors, intact extremity (IE) plantar flexors, and IE hip extensors). For the K-level, the ninth predictor was The Multidimensional Scale of Perceived Social Support (MSPSS). Conclusions: Using the SVM model, we can predict the K-level, TUG, and TMWT with high accuracy. These clinical assessments could be incorporated into routine clinical practice to guide clinicians and inform patients of their potential level of ambulation.

In this paper, a generalized particle swarm optimization (GPSO) algorithm was applied to the problems of optimal control of high speed low cost interior permanent magnet motor (IPMSM) ...