AI techniques in induction machines diagnosis including the speedripple effect

Various applications of artificial intelligence (AI) techniques (expert systems, neural networks, and fuzzy logic) presented in the literature prove that such technologies are well suited to cope with on-line diagnostic tasks for induction machines. The features of these techniques and the improvements that they introduce in the diagnostic process are recalled, showing that, in order to obtain an indication on the fault extent, faulty machine models are still essential. Moreover, by the models, that must trade off between simulation result effectiveness and simplicity, it is possible to overcome crucial points of the diagnosis. With reference to rotor electrical faults of induction machines, a new and simple procedure based on a model which includes the speed ripple effect is developed. This procedure leads to a new diagnostic index, independent of the machine operating condition and inertia value, that allows the implementation of the diagnostic system with a minimum configuration intelligence     

Monitoring of induction motor load by neural network techniques

This paper deals with the electric tracing of the load variation of an induction machine supplied by the mains. A load problem, like a torque dip, affects the machine supply current and consequently it should be possible to use the current pattern to detect features of the torque pattern, using the machine itself as a torque sensor. But current signature depends on many phenomena and misunderstandings are possible. At first the effect of different load anomalies on current spectrum, in comparison with other machine problems like rotor asymmetries, are investigated. Reference is made to low frequency torque disturbances, which cause a quasistationary machine behavior. Simplified relationships, validated by simulation results and by experimental results, are developed to address the current spectrum features. In order to detect on-line anomalies, a current signature extraction is performed by the time-frequency spectrum approach. This method allows the detection of random faults as well. Finally it is shown that a neural network approach can help the torque pattern recognition, improving the interpretation of machine anomalies effects     

The onset of CML in blastic crisis: molecular features

The cytokine tumor necrosis factor alpha (TNF alpha) was proposed to mediate obesity related insulin resistance upon production in fat cells and to participate in tissue remodelling leading to vascular complications upon being released by macrophages. To assess its putative role in diabetes we determined plasma levels of TNF alpha in 105 adult humans. Male nondiabetic subjects had significantly lower TNF alpha levels than female controls (4.4 +/- 0.3, n = 17 vs. 6.6 +/- 1.0 pg/ml, n = 13; p = 0.049). Men with NIDDM had elevated TNF alpha (6.7 +/- 0.6 pg/ml, n = 34) compared to nondiabetic subjects (4.4 +/- 0.3 pg/ml, n = 17; p = 0.012). Such a difference was not apparent in women. Levels of TNF alpha were correlated with serum triglyceride levels in male controls (r2 = 0.64; p = 0.007) but not in NIDDM. Neither body mass index nor glycosylated hemoglobin correlated with TNF alpha in any of the groups. The presence of retinopathy (p = 0.046) but not of neuropathy or nephropathy or macroangiopathy was associated with significantly elevated plasma TNF alpha. We conclude that plasma levels of TNF alpha are sex-dependent and that increased TNF alpha occurs in male but not female NIDDM and may participate in the development of diabetic complications.     

Layered Germanium Hybrid Perovskite Bromides: Insights from Experiments and First‐Principles Calculations

Metal halide perovskites are maturing as materials for efficient, yet low cost solar cells and light‐emitting diodes, with improving operational stability and reliability. To date however, most perovskite‐based devices contain Pb, which poses environmental concerns due to its toxicity; lead‐free alternatives are of importance to facilitate the development of perovskite‐based devices. Here, the germanium‐based Ruddledsen–Popper series (CH₃(CH₂)₃NH₃)₂(CH₃NH₃)_n?1Ge_nBr_3n+1 is investigated, derived from the parent 3D (n = ∞) CH₃NH₃GeBr₃ perovskite. Divalent germanium is a promising, nontoxic alternative to Pb²⁺ and the layered, 2D structure appears promising to bolster light emission, long‐term durability, and moisture tolerance. The work, which combines experiments and first principle calculations, highlights that in germanium bromide perovskites the optical bandgap is weakly affected by 2D confinement and the highly stereochemically active 4s² lone pair preludes to possible ferroelectricity, a topic still debated in Pb‐containing compounds.     

Quantitative evaluation of induction motor broken bars by means ofelectrical signature analysis

The paper reports the comparison and performance evaluation of different diagnostic procedures that use input electric signals to detect and quantify rotor breakage in induction machines supplied by the mains. Besides the traditional current signature analysis based on one-phase current spectrum lines at frequencies (1±2s)f, the procedures based on analysis of the line at frequency 2sf in the spectrum respectively of electromagnetic torque, space vector current modulus and instantaneous power are considered. These last procedures have similar features and the comparison is developed on the basis of instantaneous torque. It is addressed that the speed ripple introduces two further terms in the instantaneous torque, decreasing the accuracy of the diagnosis. It is shown that there is a link between the angular displacement of the current sideband components at frequencies (1±2s)f. This allows a more correct quantitative evaluation of the fault and shows the superiority of the side band current components diagnostic procedure over the other proposed methods     

On-field experience with online diagnosis of large induction motors cage failures using MCSA

The experience gained by ENEL Produzione (previously the Italian Electric Board) on monitoring the cage condition of large induction motors is reported in this paper. The diagnostic procedure is based on the motor current signature analysis and, in particular, on the two sideband current components near the frequency fundamental line that appear in the current power spectrum when a rotor bar/ring breakage occurs. According to the developed procedure, a diagnostic index obtained from these components is stored and its trend as a function of time allows for the detection of the occurrence of a failure in most cases. This event is clearly shown by the overcoming of a prefixed and triggered threshold. Moreover, machines with particular rotor magnetic structure are considered. In this case, unexpectedly high sideband components appear, even in the presence of healthy cages, and the test procedure was adapted to account for these conditions.     

Low-cost DSP-based equipment for the real-time detection of transients in power systems

A novel solution for the real-time detection of transients in power systems is presented. It relies on a simple and robust algorithm, which analyzes the signal in the time domain, does not involve high computational burdens, does not require a high amount of memory, and has been implemented on a low-cost DSP. Experimental results showing the very good system performance are provided.