A diagnosis sensitizer, which is a preprocessor comprising a voice input device (2) for acquiring an uttered voice, an analog/digital conversion device (3) for converting the uttered voice acquired by the voice input device (2) into digital voice data, a comparator (4) for outputting voice data over a fixed level generated from the digital voice data after the conversion by the analog/digital conversion device (3), a voice data segmenting device (11) for segmenting the voice data over a fixed level outputted from the comparator (4) in units of a phoneme, and a voice data outputting device (10) for outputting voice data in units of a phoneme outputted from the voice data segmenting device (11).

Chaos theoretical diagnosis sensitizer

The invention discloses a precision monitoring diagnosis method based on differential chaos and used for an electric spindle of an ultra-high-speed numerical-control grinding machine. The precision monitoring diagnosis method comprises the following steps: firstly, counting frequency, which corresponds to different error factors, of the spindle; then, constructing a duffing oscillator signal differential dual-chaos detecting system based on a duffing equation, respectively carrying out positive-phase input detection and reversed-phase input detection on a detected signal, carrying out vectorization and OR operation on a maximal Lyapunov index of the positive-phase input detection and the reversed-phase input detection under the same error factor, thereby obtaining discriminant parameters L, discriminating that the precision of the spindle is normal and ending this monitoring diagnosis if all discriminant parameters L are 0; otherwise, amplifying the detected signal and inputting the duffing equation in an inverted-phase mode to obtain a phase diagram discriminating track, and comparing the phase diagram discriminating track with a chaos track phase diagram under each error factor to determine the error factors. The precision monitoring diagnosis method has the advantages of being high in detecting sensitivity, remarkably reducing an omission ratio of the chaos detecting system, reducing fault factor diagnosis discriminating difficulty, greatly improving a diagnosis abduction accurate rate and the like.

Precision monitoring diagnosis method based on differential chaos and used for electric spindle of ultra-high-speed numerical-control grinding machine

PURPOSE: To obtain a correlative dimension and a Liapunov spectrum even from quasi-stationary time series data consisting of a combination of stationary time sections CONSTITUTION: A spectrum Pt0 (f) at a time t0 and a spectrum pt (f) at a time(t) for time series data x(t) (S1) are calculated (S2), and spectrum error quantity is used as a measure for the difference of them (S3) When the spectrum error quantity over a threshold value is detected (S4), segmentation is performed adaptively assuming a time until then as t1 and a stationary time section as T1 (S5) After that, an attractor is reconfigured by an embedding method for the time section T1 (S6), and hereinafter, the correlative dimension of the time section is found similarly as in a conventional method The correlative dimension is found from the quasi-stationary time series data consisting of the combination of the stationary time sections

Method and device for correlative dimension analysis, and method and device for liapunov spectrum analysis, for quasi-stationary time series using adaptive segmentation

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Method and device for correlative dimension analysis, and method and device for liapunov spectrum analysis, for quasi-stationary time series using adaptive segmentation