An analysis support apparatus includes: a feature interpreting section extracting a feature from a target analysis model generated from vibration data and classifying the feature into an abnormality determination feature, or a feature representing a vibration and enabling determining whether abnormality occurs; an abnormality determination range creating section identifying a range being determined to be abnormal of the abnormality determination feature of the target analysis model as an abnormality determination range; a similar model selecting section calculating an overlapping degree indicating how much the abnormality determination ranges of the target analysis model and one or more predetermined reference analysis models overlap and selecting, based on the overlapping degree, a similar analysis model similar to the target analysis model from the reference analysis models; and an abnormality range difference calculating section extracting a difference between the abnormality determination ranges of the target analysis model and the similar analysis model.

Monitoring of a machine is performed by an acoustic monitor which acquires, through an acoustic sensor, acoustic signals from a vicinity of a machine, while the machine is operative. A processor calculates a frequency spectrum of a segment of the acquired acoustic signals, determines boundaries of a frequency band to be analyzed and extracts, from the calculated frequency spectrum, a base frequency window in the determined boundaries, and one or more harmonics windows of harmonics of the determined boundaries. For each of the base and harmonic windows a weight based on a distribution of values of frequencies in the windows is determined and a parameter of operation of the machine is calculated as a function of a weighted sum of the base and harmonic windows. The operation of the machine is evaluated responsive to the calculated parameter.

A diagnostic apparatus includes a first acquiring unit that acquires sound information, a second acquiring unit that acquires operation information indicating a component in operation among a plurality of components of an analysis target apparatus, and a display unit that, when the acquired sound information is reproduced, displays operating states of the plurality of components at the time point when the reproduced sound is acquired, using the operation information.

An apparatus for equipment monitoring includes an input unit, a processing unit, and an output unit. The input unit is configured to provide the processing unit with batches of temporal sensor data for an item of equipment. Each batch of temporal sensor data includes temporal sensor values as a function of time. The processing unit is configured to process the batches of temporal sensor data to determine batches of spectral sensor data. Each batch of spectral sensor data includes spectral sensor values as a function of frequency. The processing unit is configured to implement at least one statistical process algorithm to process the spectral sensor values for the batches of spectral sensor data to determine index values. For each batch of spectral sensor data there is an index value determined by each of the statistical process algorithms.

A method for controlling/regulating a rotatory drive of a working unit of a track maintenance machine includes using a sensor to record a measurement parameter with an approximately periodic history function derived from a rotation of the drive. A frequency or period duration of the history function is determined with an evaluation device, and the frequency or period duration is compared with a target parameter for stipulating a control signal. A number of time-discrete measurement values is formed for the measurement parameter and an auto-correlation of these measurement values is carried out by a calculation unit for determining the frequency or the period duration. As compared to a conventional method with zero stage recording, a precise recording of frequency changes is therefore also possible between two zero stages. A device for carrying out the method is also provided.

It is provided a method for classifying vibrations detected in a structure of a building. The method is performed in a vibration classifier and comprising the steps of: determining a measurement period of a vibration signal; splitting the measurement period in a plurality of sequential sub-periods; calculating, for each one of the sub-periods, a variation indicator of at least one component of the vibration signal; and classifying a source of the vibration signal based on the variation indicators.

A method of detecting an event within a wellbore includes obtaining a sample data set, determining a plurality of frequency domain features of the sample data set, comparing the plurality of frequency domain features with an event signature, determining that the plurality of frequency domain features matches the thresholds, ranges, or both of the event signature, and determining the presence of the event within the wellbore based on determining that the plurality of frequency domain features match the thresholds, ranges, or both of the event signature. The sample data set is a sample of an acoustic signal originating within a wellbore including a fluid. The sample data set is representative of the acoustic signal across a frequency spectrum. The event signature includes a plurality of thresholds, ranges, or both corresponding to the plurality of frequency domain features.

A method of detecting an event within a wellbore includes obtaining a sample data set, determining a plurality of frequency domain features of the sample data set, comparing the plurality of frequency domain features with an event signature, determining that the plurality of frequency domain features matches the thresholds, ranges, or both of the event signature, and determining the presence of the event within the wellbore based on determining that the plurality of frequency domain features match the thresholds, ranges, or both of the event signature. The sample data set is a sample of an acoustic signal originating within a wellbore including a fluid. The sample data set is representative of the acoustic signal across a frequency spectrum. The event signature includes a plurality of thresholds, ranges, or both corresponding to the plurality of frequency domain features.

A method of detecting sand ingress within a wellbore includes obtaining a sample data set, determining a plurality of frequency domain features of the sample data set over a plurality of depth ranges, determining a presence of sand ingress at a first depth range of the plurality of depth ranges within the wellbore based on determining that the plurality of frequency domain features over the first depth range match a sand ingress signature, and determining a presence of sand migration along a second depth range of the plurality of depths within the wellbore based on determining that the plurality of frequency domain features over the second depth range match a sand migration signature. The sample data set is a sample of an acoustic signal originating within a wellbore including a fluid. The sample data set is representative of the acoustic signal across a frequency spectrum.

A vibration sensor with monitoring function is provided, which includes a substrate, a microelectromechanical vibration sensor chip and an application-specific integrated circuit chip. The microelectromechanical vibration sensor chip is disposed on the substrate and detects a vibration applied to an object to generate a plurality of vibration signals. The application-specific integrated circuit chip is disposed on the substrate and electrically connected to the microelectromechanical vibration sensor chip, which includes a sampling module, a transform module and an analysis module. The sampling module receives and converts the vibration signals into a plurality of digital signals, and filters the digital signals to generate a plurality of time-domain data. The transform module transforms the time-domain data into a frequency-domain data according to a predetermined number. The analysis module executes a comparison process to compare the frequency-domain data with a predetermined spectrum feature table and generates a notification signal according to the comparison result.