G01H1/006

Monitoring sensor for state of blade of rotating machine, position adjustment method for sensor, and rotating machine
11248489 · 2022-02-15 · ·

A monitoring sensor for a state of a blade of a rotating machine includes a sensor for monitoring a state of the blade of the rotating machine, a first section configured to be fixed to a casing of the rotating machine, and a second section holding the sensor and supported by the first section so as to be able to adjust a position of the sensor in an axial direction of the casing.

Method and device for monitoring vibrations of the winding overhang in a generator

A method for monitoring vibrations of the winding overhang in a generator (2) comprises the following steps: —detection of vibrations of the winding overhang (15) during the operation of the generator; —transformation of signals of the vibrations into the frequency range; —transformation of multiple individual vibrations from the frequency signals into the modal range; and —determination of deviations of the modal forms and/or individual bar vibrations in relation to a reference response.

Method for generating a pulse signal sequence
09766160 · 2017-09-19 · ·

A method for generating a pulse signal sequence using a processor unit is provided that allows calibrating a tip timing measurement system in a turbomachine in order to increase operational security and lifespan of the turbomachine. This is achieved by the method having the steps of: storing a number of wait time elements in a memory unit, creating a pulse signal in a signal output unit during at least one processor cycle, reading a wait time element from the memory unit, and creating a null signal in the signal output unit for a number of processor cycles derived from the wait time element read.

Method for predicting vibrations of an aircraft
11192643 · 2021-12-07 · ·

A method for predicting vibrations in an aircraft comprising an active vibration reduction system includes estimating a first vibration amplitude or frequency resulting from adjustments by the active vibration reduction system and the respective sensitivities of the aircraft depending on the flying state using a statistical mathematical process, recording a second vibration amplitude or frequency by a sensor, generating a pseudo-vibration profile by combining the first and second vibration amplitudes or frequencies, comparing the pseudo-vibration profile with a predefined target vibration profile, and outputting a signal when a specific threshold value has been exceeded.

SYSTEM AND METHOD FOR CONTROLLING A JOURNAL BEARING

A system including: a journal bearing having a carrier, a rotor arranged rotatable about a rotational axis relative to the carrier, and a fluid in a clearance between the rotor and the carrier; at least one sensor to measure a vibration signal of the rotor; a control system adapted to determine a pressure set point for the fluid in the clearance based on the vibration signal, and to provide control signals generated based on the pressure set point; and active means adapted to modify the pressure of the fluid in the clearance based on the control signals.

Method and system for measuring rotor blade tip deflection using blade tip timing (BTT)

A method (400) of measuring rotor blade tip deflections of turbomachine rotor blades (R.sub.1, R.sub.2) during operation using Blade Tip Timing (BTT) includes measuring (402), by a proximity sensor (202), a proximity signal caused by a moving rotor blade (R.sub.1, R.sub.2) and determining (412), by a control module (212), a Time-of-Arrival (ToA). The method (400) includes measuring a time that the shaft starts to rotate a measurable angular distance and a time the shaft has completed its rotation of the measurable distance and storing (406) timing data indicative of a plurality of ToA measurements of the rotor blade (R.sub.1, R.sub.2) and the zero crossing times. The method (400) includes determining the shaft Instantaneous Angular Position (IAP) between at least two zero crossing times, which determination comprises expressing the shaft IAP between at least two zero crossing times as a continuous, non-constant IAP mathematical function of time, with unknown function coefficients and calculating the unknown function coefficients of the IAP mathematical function.

Method of identifying fault in synchronous reluctance electric machine, monitoring system and synchronous reluctance electric machine

A method of identifying a fault in a synchronous reluctance electric machine, the method including carrying out a first vibration measurement on a stator in a first radial direction of the stator; carrying out a second vibration measurement on the stator in a second radial direction of the stator; determining, on the basis of at least one of the first vibration measurement and the second vibration measurement, a first vibration frequency; determining, on the basis of the first vibration measurement and the second vibration measurement, a mode shape of the vibration at the first vibration frequency; and determining, on the basis that the first vibration frequency f.sub.b and the mode shape m fulfil the following barrier fault conditions:
f.sub.b=f.sub.r, and m=1
where f.sub.r is a rotation frequency of a rotor, that a flux barrier of the rotor is defect.

Shaft monitoring system
11313245 · 2022-04-26 · ·

A monitoring system for monitoring one or more properties associated with a rotating shaft is provided. The system includes a first phonic wheel which is mounted coaxially to the shaft for rotation therewith, the first phonic wheel comprising a circumferential row of teeth. The system further includes a first sensor configured to detect the passage of the row of teeth of the first phonic wheel by generating a first alternating measurement signal. The system further includes a processor unit configured to determine the durations of successive first speed samples. Each first speed sample is a block of n successive cycles of the first alternating measurement signal, where n is an integer, and in which the beginning of each cycle is a zero-crossing point from the previous cycle and the end of each cycle is the corresponding zero-crossing point to the next cycle. At least one axial location of the first phonic wheel every m.sup.th tooth of the row of teeth of the first phonic wheel has a circumferential thickness which is different from that of the other teeth of the first phonic wheel, where m is an integer, m≠n, and m is neither a factor nor a multiple of n. When the first sensor is positioned at said axial location of the first phonic wheel and at any given rotational speed of the first phonic wheel, the durations of the successive first speed samples display a characteristic repeating pattern of longer and shorter sample durations relative to the average duration of the successive first speed samples. The amount by which the longer and shorter sample durations differ from the average duration is in proportion to the amount by which the circumferential thickness of the m.sup.th teeth differs from that of the other teeth at said axial location of the first phonic wheel. The processor unit monitors the properties associated with the rotating shaft from the characteristic repeating pattern.

Pump with vibration sensor and its production process
20230243358 · 2023-08-03 ·

This pump includes a computing device, with a vibration waveform analysis section and a memory, the analysis section being designed to control the motor, by comparing a vibration waveform received from the vibration sensor with vibration waveforms saved in the memory.

Prediction method of part surface roughness and tool wear based on multi-task learning

A prediction method of part surface roughness and tool wear based on multi-task learning belong to the file of machining technology. Firstly, the vibration signals in the machining process are collected; next, the part surface roughness and tool wear are measured, and the measured results are corresponding to the vibration signals respectively; secondly, the samples are expanded, the features are extracted and normalized; then, a multi-task prediction model based on deep belief networks (DBN) is constructed, and the part surface roughness and tool wear are taken as the output of the model, and the features are extracted as the input to establish the multi-task DBN prediction model; finally, the vibration signals are input into the multi-task prediction model to predict the surface roughness and tool wear.