A sound recognition apparatus (100) includes at least two microphones (1) that detect a surrounding sound, an attitude detection unit (4) that detects attitudes of the microphones (1), a self-position estimation unit (52) that estimates positions of the microphones (1) based on the attitudes of the microphones (1) and a signal of the sound detected by the microphone (1), a sound source estimation unit (53) that estimates a direction of the sound source (200) of the sound based on the attitudes of the microphones (1) and the signal of the sound detected by the microphone (1), and a sound environment mapping unit (54) that creates a sound environment map based on the positions of the microphones (1) and the direction of the sound source (200), the sound environment map displaying at least a position of the sound source (200).

A vapor and/or gas concentration and temperature sensor includes a resonating structure having a first side with a functionalized surface and a second side opposite the first side, a first resonant frequency of a first vibration mode, and a second resonating frequency of a second vibration mode. Drive and sensing electrodes face the second side of the resonating structure. A direct current bias source is coupled to the resonating structure. A first AC voltage source provides the resonating structure with a first voltage having a frequency corresponding to the first resonant frequency. A second AC voltage source provides the resonating structure with a second voltage having a frequency corresponding to the second resonant frequency. A read-out circuit determines a vapor and/or gas concentration based on a difference between the frequency of the first voltage and a first read-out frequency and determines a temperature based on a difference between the frequency of the second voltage and a second read-out frequency.

Provided is a fault signal locating and identifying method of industrial equipment based on a microphone array. The method includes the steps of: acquiring sound signals and dividing the acquired signals into a training set, a verifying set and a test set; performing feature extraction on the sound signals in the training set, and extracting a phase spectrogram and an amplitude spectrogram of a spectrogram; sending an output of a feature extraction module, as an input, to a CNN, and in each layer of the CNN, learning a translation invariance in the spectrogram by using a 2D CNN; in between the layers of the CNN, normalizing the output by using a batch normalization, and reducing a dimension by using a maximum pooling layer along a frequency axis; sending an output from the layers of the CNN to layers of RNN; using a linear activation function; and inputting an output of a full connection layer to two parallel full connection layer branches for fault identification and fault location, respectively.

The present invention prevents stoppage or a disruptive accident during operation due to a breakdown in machinery. A waveform analysis device 200 comprises: a sensor unit 300 for detecting a physical phenomenon; a discrete Fourier transform unit 208 for performing a discrete Fourier transform of a detection signal transmitted from the sensor unit 203; a later-stage weighting unit 209 for setting amplitude values at each frequency generated by the discrete Fourier transform unit 208 that exceed a prescribed upper-limit value to said prescribed upper-limit value; and an accumulation unit 210 for adding the amplitude values at each frequency weighted by the later-stage weighting unit 209. An operator console 100 sets the prescribed upper-limit value in the waveform analysis device 200.

An abnormality determination method for a wind power generation device includes: a measurement step (step S1) of measuring sound emitted by the wind power generation device and recording acoustic data; an analysis step (step S2) of performing a spectrogram analysis on the acoustic data recorded in the measurement step, on a frequency axis and in a temporal axis space as a temporal change in a frequency characteristic by using the short-time Fourier transform or the wavelet transform; a detection step (step S3) of detecting, from the analysis result in the analysis step, a signal component emitted from an abnormal portion of the wind power generation device in a time corresponding to rotation of the wind power generation device; and a determination step (step S5) of determining that the wind power generation device is abnormal when the signal component detected in the detection step is greater than or equal to a certain threshold value.

Systems, methods, and other embodiments associated with acoustic detection of changes in mass of cargo carried by a vehicle are described herein. In one example method for acoustic cargo surveillance, a first acoustic output of a vehicle carrying cargo at a first time of surveillance of the vehicle is recorded. Then, a second acoustic output of the vehicle at a subsequent time in the surveillance of the vehicle carrying the cargo is recorded. A change in a mass of the cargo carried by the vehicle is acoustically detected based at least on an acoustic change between the first acoustic output and the second acoustic output. An electronic alert is generated that the mass of the cargo has changed based on the acoustic change.

Systems, methods, and other embodiments associated with acoustic detection of changes in mass of cargo carried by a vehicle are described herein. In one example method for acoustic cargo surveillance, a first acoustic output of a vehicle carrying cargo at a first time of surveillance of the vehicle is recorded. Then, a second acoustic output of the vehicle at a subsequent time in the surveillance of the vehicle carrying the cargo is recorded. A change in a mass of the cargo carried by the vehicle is acoustically detected based at least on an acoustic change between the first acoustic output and the second acoustic output. An electronic alert is generated that the mass of the cargo has changed based on the acoustic change.

Systems, methods, and other embodiments associated with acoustic detection of disguised vehicles are described. In one embodiment of a method for acoustic detection of disguised vehicles, a first acoustic output of a target vehicle that appears to be of a first type is recorded. A second acoustic output of a reference vehicle that is known to be of the first type is retrieved. It is acoustically detected that the target vehicle is not of the first type based at least on an acoustic dissimilarity between the first acoustic output and the second acoustic output. An electronic alert is then generated that the target vehicle is of a second type that is disguised as the first type based on the acoustic dissimilarity.

Systems, methods, and other embodiments associated with acoustic detection of disguised vehicles are described. In one embodiment of a method for acoustic detection of disguised vehicles, a first acoustic output of a target vehicle that appears to be of a first type is recorded. A second acoustic output of a reference vehicle that is known to be of the first type is retrieved. It is acoustically detected that the target vehicle is not of the first type based at least on an acoustic dissimilarity between the first acoustic output and the second acoustic output. An electronic alert is then generated that the target vehicle is of a second type that is disguised as the first type based on the acoustic dissimilarity.

A data processing device includes a peak detector that detects a peak from a frequency spectrum and a map generator that generates an abnormality map for the frequency spectrum. The abnormality map includes as abnormal components, a frequency of a detected peak of interest and a frequency of a peak that appears together with the peak of interest when the peak of interest is assumed as the peak originating from abnormality. The data processing device includes an abnormal peak extractor that extracts as an abnormal peak, a peak at a frequency that matches with any of the abnormal components included in the abnormality map and a first criterion value calculator that calculates a first criterion value representing occurrence of abnormality corresponding to the abnormality map based on a spectral density of the abnormal peak.