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.

Systems, methods, and computer-readable media are disclosed for contactless power converter aging detection. An example method may include receiving, from a microphone, first acoustic data associated with a power converter. The example method may also include converting the first acoustic data into second acoustic data, wherein the first acoustic data is time domain data and the second acoustic data is frequency domain data. The example method may also include determining, by a machine learning model and based on the second acoustic data, a remaining useful life value associated with the power converter.

Systems, methods, and computer-readable media are disclosed for contactless power converter aging detection. An example method may include receiving, from a microphone, first acoustic data associated with a power converter. The example method may also include converting the first acoustic data into second acoustic data, wherein the first acoustic data is time domain data and the second acoustic data is frequency domain data. The example method may also include determining, by a machine learning model and based on the second acoustic data, a remaining useful life value associated with the power converter.

An abnormality predicting system includes a processor and a memory having instructions. The instructions, when executed by the at least one processor, cause the at least one processor to execute operations including: inputting processing target data acquired from a target device; storing information related to an abnormality prediction of the processing target data; calculating an abnormality degree of the processing target data; executing processing related to the abnormality prediction including a failure occurrence prediction using a latest abnormality degree transition and a past abnormality degree transition of the processing target data; and generating a display screen for displaying a processing result including an abnormality degree transition and a result of the failure occurrence prediction.

An image processing apparatus includes a controller. The controller calculates a fundamental frequency component included in sound data and a harmonic component corresponding to the fundamental frequency component, converts the fundamental frequency component and the harmonic component into image data, and generates a sound image where the fundamental frequency component and the harmonic component converted into the image data are arranged adjacent each other.

An abnormal sound identification device includes an arithmetic device and an output device. The arithmetic device is configured to identify frequency-time data recorded in a vehicle, specify a first time range and a second time range in the frequency-time data, input the frequency-time data to the trained model to cause the trained model to identify an abnormal sound generated in the first time range as a first abnormal sound based on the input frequency-time data and cause the trained model to identify an abnormal sound generated in the second time range as a second abnormal sound, and cause the output device to output a kind of the first abnormal sound with the kind not matching a kind of the second abnormal sound among the first abnormal sounds.

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.

Data retrieval, event recording and transmission module connectable to safety and relief valves. The module (10) coupled to safety or relief valves for the measurement, transmission and recording of events and includes a rigid and inextensible at room temperature longitudinal element defining an acoustic emission waveguide (12) that transmits vibrations. The end (12a) of (12) is in intimate contact with a portion (2) or (1) of the valve, and its other end (12b) penetrates inside a closed resistant box (10), wherein it sits in contact against at least one acoustic emission sensor (13) receiving a signal proportional to the vibrations transmitted by the waveguide (12).

A system, method, and non-transitory computer readable medium for diagnosing stator inter-turn faults in a Line Start Permanent Magnet Synchronous Motor (LSPMSM) are described. The method of diagnosing stator inter-turn faults in the LSPMSM includes collecting acoustic signals that are generated from a LSPMSM by a communication device, analyzing via singular spectrum analysis (SSA) the collected acoustic signals for fault detection of the stator inter-turn faults, and determining a fault diagnosis for the fault detection by executing a Fast Fourier Transform (FFT).

A method of automatically setting an acoustic signal of a device for space monitoring by using the acoustic signal is proposed. The proposed is a technology for automatically setting the acoustic signal emitted from the device for space monitoring by using the acoustic signal for more precise measurement.