An AE-signal detecting device for an abrasive wheel includes: an AE sensor which outputs an AE signal upon receipt of an elastic wave generated in an annular abrasive wheel sandwiched between a fixed flange fixed to a rotating shaft and a movable flange provided capable of getting closer to/separating from the fixed flange; a transmission circuit portion which wirelessly transmits the AE signal output from the AE sensor; and a reception circuit portion which receives the AE signal transmitted wirelessly, wherein the AE sensor is disposed on the movable flange or the fixed flange, detects the elastic wave transmitted from the abrasive wheel, and outputs the AE signal.

A monitoring device for detecting a diagnostic state of an industrial machine on the basis of vibrations associated with the industrial machine, includes at least one measurement transducer for detecting at least one first signal characterizing the vibration of the industrial machine in a time period and for detecting at least one second signal characterizing the vibration of the industrial machine in the same time period, wherein the at least one measurement transducer has at least one signal processor for evaluating the at least first signal as a first characteristic value and for evaluating the at least second signal as a second characteristic value and wherein the at least one first characteristic value and the at least one second characteristic value form a characteristic value tuple, further having a storage unit, in which different diagnostic states describing the state of the industrial machine are saved, wherein the diagnostic states are determined as a function of the characteristic value tuple, and a predefined scalar range is saved with predetermined different fixed scalar values, which define respectively an existing limit potential, wherein a respective limit potential characterizes a change in the diagnostic state, and wherein between adjacent limit potentials a diagnostic partition is formed and wherein different scalar values from the scalar range are assigned to the different diagnostic partitions and wherein different respective diagnostic partitions represent different diagnostic states of the industrial machine, and wherein the monitoring device has at least one interpolation function for mapping the characteristic value tuple on a common, unique, time-dependent scalar value in the predefined scalar range, so that by way of the time-dependent scalar value a time monitoring of the diagnostic state of the industrial machine is achieved.

A road inclination estimating apparatus is configured to acquire power spectrum density of vertical vibration by a frequency analysis based on the detected vertical acceleration of the sprung mass of a vehicle. The apparatus is configured to determine that a first estimation condition is satisfied, when the power spectrum density has two of the acquired peak frequencies, wherein, one of the two of the peak frequencies is within a predetermined first frequency range, and the other one of the two of the peak frequencies is within a predetermined second frequency range.

A method for automatic diagnosis of a mechanical system of a group of mechanical systems sharing mechanical characteristics includes obtaining data relating to a vibration. The vibration-related data is acquired by a portable communications device configured to communicate with a remote processor. The processor automatically diagnoses the mechanical system by applying a relationship to the obtained vibration-related data. The relationship is based on sets of vibration-related data previously obtained from the mechanical systems. Each set of vibration-related data relates to vibrations of a mechanical system. The relationship is further based on sets of operation data previously obtained for mechanical systems of the group. Each set of operation data indicates a previous state of operation of a mechanical system. Each of the previous states of operation is associated with at least one of the previously obtained sets of vibration-related data.

An information terminal is configured to extract a predetermined frequency band from a waveform of a signal of a machine component detected by a vibration sensor, to compare an analyzed frequency component and a damaging frequency resulting from a damage of the machine component, and to diagnose abnormality of the machine component. The information terminal includes a database in which the damaging frequency resulting from the damage of the machine component is preserved as a converted damaging frequency obtained by converting the damaging frequency on a basis of a predetermined rotating speed of the machine component. The damaging frequency is provided by calculating the converted damaging frequency in the database by using an actual rotating speed of the machine component. Accordingly, it is possible to keep specifications of a machine component confidential without preserving the specifications of the machine component.

A noise canceler includes a signal obtaining unit that obtains an observation signal obtained by sampling vibration of an apparatus; a signal delay unit that obtains a delay signal by delaying the observation signal obtained by the signal obtaining unit; and a Kalman filter processing unit that applies, to the observation signal and the delay signal, a Kalman filter that adapts a filter coefficient so as to reduce a residual signal between the observation signal and an estimated signal estimated based on the observation signal and the delay signal by an adaptive filter that incorporates the Kalman filter, and outputs the residual signal.

A monitoring device includes a process data acquisition unit configured to acquire process data indicating an operation condition of a machine having a rotating shaft, a shaft vibration value acquisition unit configured to acquire a measurement value of a shaft vibration value of the rotating shaft under the operation condition indicated by the process data, a determination model configured to determine a normal value of the shaft vibration value according to the operation condition created on the basis of the shaft vibration value measured during an operation of the machine and the shaft vibration value calculated on the basis of a predetermined shaft vibration calculation model, and a monitoring unit configured to evaluate the measurement value of the shaft vibration value on the basis of the process data, the measurement value of the shaft vibration value, and the determination model.

The invention provides a method for monitoring health state of blade root fastener, comprising the following steps: obtaining a sequence of acceleration signals representing the lateral vibration of the nacelle and a sequence of rotational speed signals representing the rotational speed of the rotor; analyzing the sequence of acceleration signals and the sequence of rotational speed signals to determine the amplitude of the nacelle at 2-time-frequncy of the rotational speed of the rotor; and determining the health state of the blade root fastener based on the amplitude. The invention also provides a system for monitoring the health state of the blade root fastener. Through the present invention, the health state of the blade root fastener can be determined with low cost and high precision, thereby improving the operation efficiency and operation safety of the wind turbine.

A method includes receiving data characterizing time-dependent lateral vibration of a shaft of a machine, the lateral vibration indicative of motion of at least a portion of the shaft perpendicular to a first direction. The lateral vibration is detected by a first sensor located at a first predetermined location on the shaft. The method further includes, receiving data characterizing time-dependent torsional vibration of the shaft, the torsional vibration indicative of rotation of the shaft around the first direction. The torsional vibration is detected by a second sensor located at a second predetermined location on the shaft. The method also includes calculating a coherence of the data characterizing time-dependent lateral vibration and the data characterizing time-dependent torsional vibration. The method further includes identifying, based on the coherence, a first frequency value in the frequency domain indicative of coupling between the time-dependent lateral vibration and the time-dependent torsional vibration.

Technologies are generally described for a vibration band alarm configuration tool to facilitate efficient and reliable setting configuration by displaying data and analyses pertaining to the data produced by a vibration transducer attached to a rotating machine and thereby reduces needed knowledge of machinery and vibration analysis. Various parameters and data sets may be calculated from the time domain waveform and displayed with collected data to simplify the recognition of features that represent system components and anomalies pertinent to the health of the rotating machine such as a pump assembly. Manually manipulatable controls on a graphic user interface may allow a user to set alarm bands and thresholds. Suggestions based on physical and/or statistical models, as well as, machine-specific historic data may be presented as well.