Both a hologram and fluorescence are simultaneously captured in a state in which they can be reconstructed separately. A recording device (10) includes: a laser light source (LS1) which irradiates a subject (13) with object illumination light so that object light is generated; and an image capturing device (12) which captures (i) a hologram formed by interference between reference light and object light and (ii) an image of fluorescence, and the object illumination light further excites a fluorescent material (14) contained in the subject (13).

Both a hologram and fluorescence are simultaneously captured in a state in which they can be reconstructed separately. A recording device (10) includes: a laser light source (LS1) which irradiates a subject (13) with object illumination light so that object light is generated; and an image capturing device (12) which captures (i) a hologram formed by interference between reference light and object light and (ii) an image of fluorescence, and the object illumination light further excites a fluorescent material (14) contained in the subject (13).

A “periodic signal parameter” (PSP) indicates periodic patterns in an autocorrelated vibration waveform and potential faults in a monitored machine. The PSP is calculated based on statistical measures derived from an autocorrelation waveform and characteristics of an associated vibration waveform. The PSP provides an indication of periodicity and a generalization of potential fault, whereas characteristics of the associated waveform indicate severity. A “periodic information plot” (PIP) is derived from a vibration signal processed using two analysis techniques to produce two X-Y graphs of the signal data that share a common X-axis. The PIP is created by correlating the Y-values on the two graphs based on the corresponding X-value. The amplitudes of Y-values in the PIP is derived from the two source graphs by multiplication, taking a ratio, averaging, or keeping the maximum value.

A “periodic signal parameter” (PSP) indicates periodic patterns in an autocorrelated vibration waveform and potential faults in a monitored machine. The PSP is calculated based on statistical measures derived from an autocorrelation waveform and characteristics of an associated vibration waveform. The PSP provides an indication of periodicity and a generalization of potential fault, whereas characteristics of the associated waveform indicate severity. A “periodic information plot” (PIP) is derived from a vibration signal processed using two analysis techniques to produce two X-Y graphs of the signal data that share a common X-axis. The PIP is created by correlating the Y-values on the two graphs based on the corresponding X-value. The amplitudes of Y-values in the PIP is derived from the two source graphs by multiplication, taking a ratio, averaging, or keeping the maximum value.

Apparatus for monitoring an axle of a wheelset assembly of a railway vehicle, the apparatus comprising a wireless sensor node fitted to a wheelset assembly, the wheelset assembly comprising an axle mounted between opposed wheels, each wheel being fitted to a respective opposite end of the axle, the wireless sensor node comprising a vibration energy harvester for converting mechanical energy from vibration in the wheelset assembly into electrical energy, a sensor for measuring a parameter, and a wireless transmitter for wirelessly transmitting the measured parameter or data associated therewith, and the apparatus further comprising a processor for processing the measured parameter to produce processed data, wherein the sensor is an accelerometer mounted to an end of the axle and the sensor and processor are arranged respectively to measure and process an axle percussion vibration frequency in the form of resonant vibration along the axle.

Apparatus for monitoring an axle of a wheelset assembly of a railway vehicle, the apparatus comprising a wireless sensor node fitted to a wheelset assembly, the wheelset assembly comprising an axle mounted between opposed wheels, each wheel being fitted to a respective opposite end of the axle, the wireless sensor node comprising a vibration energy harvester for converting mechanical energy from vibration in the wheelset assembly into electrical energy, a sensor for measuring a parameter, and a wireless transmitter for wirelessly transmitting the measured parameter or data associated therewith, and the apparatus further comprising a processor for processing the measured parameter to produce processed data, wherein the sensor is an accelerometer mounted to an end of the axle and the sensor and processor are arranged respectively to measure and process an axle percussion vibration frequency in the form of resonant vibration along the axle.

A frequency domain feedforward compensation method based on a power exponential method for a seismic simulation shaking table is provided. According to the method, a frequency domain amplitude transfer function of a system is identified, the frequency domain amplitude transfer function is modified by adjusting the power and limiting an amplitude, then an inverse frequency domain amplitude transfer function is obtained, an amplitude and a phase of a driving acceleration signal are computed, finally a time domain driving acceleration signal is obtained by using the Euler's formula for computation in a complex domain and conducting inverse Fourier transform, the shaking table is driven again to collect an acceleration signal output by a table top, whether the acceleration signal meets a shaking table test waveform use requirement is determined, a test is finished under the condition that the acceleration signal meets the shaking table test waveform use requirement.

A method for diagnosing a problematic noise source based on big data information include: measuring noise data of a powertrain of a vehicle by using a real-time noise measurement device, and converting the noise data into a signal that can be input to a portable device for diagnosing the problematic noise source through an interface device; analyzing a noise through a deep learning algorithm of an artificial intelligence on a converted signal, and diagnosing the problematic noise source as a cause of the noise; and displaying the cause of the noise by outputting a diagnostic result as the problematic noise source, and transmitting the diagnostic result to the portable device.

Provided are a waveform analysis method and a waveform analysis device capable of preventing, in advance, a breakage accident during operation and preventing stoppage due to breakdown of machinery and performing efficient maintenance work by specifying a degraded part from among the parts that constitute the machinery. A waveform analysis device 30 is provided with; a signal analysis unit 31 for performing fast Fourier transform for a signal transmitted from a sensor 28 that detects a physical phenomenon in the machinery an impulse extraction unit 32 for extracting an impulse component from spectrum data generated by the signal analysis unit 31; a display unit 35 for displaying waveform data including the impulse component extracted by the impulse extraction unit 32; and a data editing unit 33 for editing, from data of a waveform including the impulse component displayed by the display unit 35, waveform data in a range selected via an input unit 36 by a worker, generating a graph displaying a frequency, a time, and the intensity of the impulse component, and displaying the graph on the display unit 35.

Provided are a waveform analysis method and a waveform analysis device capable of preventing, in advance, a breakage accident during operation and preventing stoppage due to breakdown of machinery and performing efficient maintenance work by specifying a degraded part from among the parts that constitute the machinery. A waveform analysis device 30 is provided with; a signal analysis unit 31 for performing fast Fourier transform for a signal transmitted from a sensor 28 that detects a physical phenomenon in the machinery an impulse extraction unit 32 for extracting an impulse component from spectrum data generated by the signal analysis unit 31; a display unit 35 for displaying waveform data including the impulse component extracted by the impulse extraction unit 32; and a data editing unit 33 for editing, from data of a waveform including the impulse component displayed by the display unit 35, waveform data in a range selected via an input unit 36 by a worker, generating a graph displaying a frequency, a time, and the intensity of the impulse component, and displaying the graph on the display unit 35.