A method for super-resolution photoacoustic microscopy of an object. The method includes optically exciting the object according to a plurality of excitation patterns utilizing a digital micromirror device (DMD), receiving a plurality of acoustic waves propagated from the object due to optically exciting the object, reconstructing each of a plurality of photoacoustic (PA) images from a respective acoustic wave of the plurality of acoustic waves, and obtaining a super-resolution PA image of the object from the plurality of PA images by applying a frequency domain reconstruction method to the plurality of PA images. Each of the plurality of acoustic waves are associated with a respective excitation pattern of the plurality of excitation patterns.

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.

An apparatus for detecting a transient event in an operating machine, the apparatus comprising: a controller configured to control performance of the following steps: a measurement step comprising measuring a periodic signal from a machine; a processing step comprising synchronously processing the periodic signal to track the primary frequency; a filtering step comprising removing the primary periodic component and its harmonics from the periodic signal to yield a filtered dataset; an integration step comprising integrating the filtered dataset over the remaining frequencies to yield an integrated dataset representing the periodic energy at frequencies other than the primary frequency and its harmonics; an analysis step comprising identifying a short-term transient in the integrated dataset to identify a transient disruption in the operation of the machine.

An apparatus for detecting a transient event in an operating machine, the apparatus comprising: a controller configured to control performance of the following steps: a measurement step comprising measuring a periodic signal from a machine; a processing step comprising synchronously processing the periodic signal to track the primary frequency; a filtering step comprising removing the primary periodic component and its harmonics from the periodic signal to yield a filtered dataset; an integration step comprising integrating the filtered dataset over the remaining frequencies to yield an integrated dataset representing the periodic energy at frequencies other than the primary frequency and its harmonics; an analysis step comprising identifying a short-term transient in the integrated dataset to identify a transient disruption in the operation of the machine.

An inspection robot, and methods and a controller thereof are disclosed. An inspection robot may include an inspection chassis including a plurality of inspection sensors and coupled to at least one drive module to drive the robot over an inspection surface. The inspection robot may also include a controller including an inspection data circuit to interpret inspection base data, an inspection processing circuit to determine refined inspection data, and an inspection configuration circuit to determine an inspection response value in response to the refined inspection data. The controller may further include an inspection response circuit to, in response to the inspection response value, provide an inspection command value while the inspection robot is interrogating the inspection surface.

A method and apparatus for inspecting a fusion joint is provided. The apparatus includes a processor, an ultrasound (“US”) probe in communication with the processor, and a database comprising classification rules. The processor is configured to generate an initial set of US scanning positions about the fusion joint based on information of at least one of the US probe and the fusion joint; measure, via the US probe, a US pulse-echo spectrum from at least two of the initial US scanning positions; compare each measured US pulse-echo spectrum with one or more known US pulse-echo spectrums; classify each measured US pulse-echo spectrum according to the classification rules; and evaluate an aggregate of measured US pulse-echo spectrums to determine if the fusion joint is defective.

A computer-implemented process determines, based on bearing fault frequencies, optimum values for the maximum frequency (F.sub.max) and the number of lines of resolution (N.sub.lines) to be used in collecting machine vibration data so as to adequately distinguish between spectral peaks for identifying faults in machine bearings. The process can be extended to any other types of fault frequencies that a machine may exhibit, such as motor fault frequencies, pump/fan fault frequencies, and gear mesh fault frequencies. Embodiments of the process also ensure that the time needed to acquire the waveform is optimized. This is particularly useful when collecting data using portable vibration monitoring devices.

A computer-implemented process determines, based on bearing fault frequencies, optimum values for the maximum frequency (F.sub.max) and the number of lines of resolution (N.sub.lines) to be used in collecting machine vibration data so as to adequately distinguish between spectral peaks for identifying faults in machine bearings. The process can be extended to any other types of fault frequencies that a machine may exhibit, such as motor fault frequencies, pump/fan fault frequencies, and gear mesh fault frequencies. Embodiments of the process also ensure that the time needed to acquire the waveform is optimized. This is particularly useful when collecting data using portable vibration monitoring devices.

A computer-implemented method of Condition Monitoring (CM) for rotating machines like motors, a corresponding computer program, computer-readable medium and data processing system for CM for rotating machines as well as a system including the data processing system for CM for rotating machines. M accumulator variables are updated in real-time based on L samples including a current sample sn and at least one preceding sample Sn−1 of input data. Based on the M accumulator variables N spectral features are computed in real-time. A condition of the rotating machine is determined based on the N spectral features.