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Induction motors have nowadays been more frequently used with frequency converters. The use of frequency converters affects the behavior of the machine and makes fault diagnosis more complicated. One of the widely used diagnostic methods is motor current signature analysis (MCSA). This method is based on measurement of sidebands in the stator current spectrum. Those sidebands are usually located close to the main supply frequency. Frequency converter causes supply frequency to slightly vary in time and, as a result, some additional harmonics in the current spectrum are induced and sidebands are reduced. Those harmonics can be easily misinterpreted as the sidebands caused by the rotor faults. Although the rotor faults in the case of frequency converter supply are not so often, precaution is still necessary. In this paper the experimental results of fault diagnosis carried out using standard supply and using frequency converter have been compared and presented. All tests were performed on 22 kW, 380 V, 1470 r/min induction motor. The current spectra are given for the motor with two broken bars with both types of supply.

This paper presents one of the maintenance test methods and the results of its application in the determination of the insulation condition of HV electric motors. Testing and data processing were carried out on site using a PC. In our tests the concept of the H"ofler method is used. Highly stabilized dc voltage as a series of voltage steps at $0.3 U_{; ; n}; ; $, $0.6 U_{; ; n}; ; $, $0.9 U_{; ; n}; ; $, $1.2 U_{; ; n}; ; $ and $1.5 U_{; ; n}; ; $ is applied to the motor windings, while at the same time the leakage currents are being measured. In the case of `ideal insulation', leakage currents are proportional to the applied dc voltage, ie the insulation resistance is independent of the applied voltage. In reality, insulation resistance and leakage currents depend on the applied voltage. The ways the leakage current depends on voltage and its magnitude can be used to determine the insulation condition, based on the nonlinearity. In the last five years over 100 on-site tests have been performed on motors with nominal voltage >6 kV and power ratings to 7 MW. The selected test voltage is $1.5 U_{; ; n}; ; $ with the consideration that testing should be virtually non-destructive and still provide a reliable assessment of the insulation condition. The test voltage may not exceed $1.5 U_{; ; n}; ; $, and the test shall be terminated at any time in case of sudden increase in leakage current indicative of a serious risk of breakdown. Depending on the nonlinearity at a particular dc voltage, the insulation condition is assessed. Motors, $sim 4$%, with nonlinearity coefficient >2 at test-voltage $1.5 U_{; ; n}; ; $ (at ambient temperature) were considered to be in non-satisfactory condition and have been sent for further testing and any necessary repairs. Cracks, filled with contaminant in aged micaceous insulation of stator windings and slip ring rotor windings, have been detected. Although there is a decrease in popularity of dc testing world wide, we hope that the presented method can be an improvement of conventional dc test methods.

This paper refers to a machine-mediated person-to-person multilingual communication system. Stress is put on robustness, that is the ability of the system to preserve communication even in presence of the variability and errors typical of spoken language systems. The statistical approach is adopted not only at the acoustic level, but also for the linguistic processing. Therefore, while an overview of the global architecture is briefly introduced, the focus is put on the acoustic recognizer and the understanding module. Experimental evaluations complete the presentation.

This paper presents a speech-to-speech translation system for tourism application developed in the context of the C-STAR consortium. Potential users can communicate by speech and by using their own language with a travel agent in order to organize their travel. The system uses an interchange format representation of the semantic contents of utterances, which is flexible and simplifies the system portability to new languages. A demonstrative prototype, developed at ITC-irst, is now working for the Italian modules and was integrated with the English counter part developed at the Interactive System Laboratory at CMU.

In spoken language systems, the classification of coherent linguistic/semantic phrases in terms of semantic classes is an important part of the whole understanding process. Basically, it relies on the plain text of the segment to be classified. Nevertheless, another important source of useful information is the dialogue context. In this paper, a number of different ways to integrate the dialogue history into the semantic classification are presented and tested on a corpus of person-to-person dialogues. Best result gives a 3.6% reduction of the error rate with respect to the performance obtained without using history.

An important problem in automatic speech understanding is the transport of an existing application system to a new language. Design choices are required to keep the cost and implementation time of the porting as low as possible. One of the bottlenecks in spoken language system development is represented by data collection. When moving an application from the original language to a new one, it is very important to exploit, as much as possible, the data collected in the first language. This paper discusses the construction of a speech-based Air Travel Information Service (ATIS) for Italian, starting from (American) English. The aim of the work is to maximize the use of data available in English for the ATIS task in the construction of the Italian system. All components will be examined and proposed strategies will be evaluated by experimental tests. By just using a small Italian corpus and the ATIS data available in English, an understanding error rate of 6·7% was obtained on Italian written transcriptions. By adapting to speakers acoustic models and by training a language model on translations of transcriptions, an understanding error rate of 17·9% was obtained by considering read as well as spontaneous Italian sentences.

The paper presents a mixed approach to spoken language understanding that tries to make best use of the advantages of both statistical and knowledge-based algorithms. Results obtained on the ATIS (Air Travel Information System) scenario transferred to the Italian language are presented and discussed.