Provided is a knocking detection apparatus capable of reducing erroneous determination of knocking during transition, and improving knocking detection accuracy regardless of whether an engine is in steady operation or transient operation. A knocking detection apparatus according to the present invention estimates a background level based on an operating state of an internal combustion engine, and calculates a knocking determination index by using the estimated value.

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.

A method is provided for testing noise of vehicles when driving over a test track. This method includes carrying out noise measurements in a vehicle driving over the test track by means of sensors arranged inside the vehicle, determining the positions of the vehicle driving on the test track and assigning the respective measurement to the position of the vehicle on the test track, evaluating the measurement results of the noise measurements, based on the respective test track position, during a comparison with known noise characteristics of comparison vehicles. The method enables an efficient objective noise test of vehicles when driving over a test track.

A method is provided for testing noise of vehicles when driving over a test track. This method includes carrying out noise measurements in a vehicle driving over the test track by means of sensors arranged inside the vehicle, determining the positions of the vehicle driving on the test track and assigning the respective measurement to the position of the vehicle on the test track, evaluating the measurement results of the noise measurements, based on the respective test track position, during a comparison with known noise characteristics of comparison vehicles. The method enables an efficient objective noise test of vehicles when driving over a test track.

An abnormal noise determination apparatus including a microphone array disposed inside a vehicle and an electronic control unit including a microprocessor. The microprocessor is configured to perform acquiring an abnormal noise data on an abnormal noise generated by a sound source disposed in a predetermined position inside the vehicle, the abnormal noise data including an information on a strength and a generation direction of the abnormal noise collected by the microphone array in advance or assumed to be collected by the microphone array; acquiring a traveling noise data including an information on a strength and a generation direction of a traveling noise collected by the microphone array during traveling of the vehicle; and determining whether the abnormal noise is included in the traveling noise of the vehicle, based on the abnormal noise data acquired and the traveling noise data acquired.

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.

In an embodiment, one or more computer-readable storage medium comprising a plurality of instructions to cause an apparatus, in response to execution by one or more processors of the apparatus, to receive sounds emanating from one or more motors included in an unmanned aerial vehicle (UAV) during operation of the one or more motors; predict a number of operational cycles remaining before the one or more motors is to fail based on analysis of the sounds; and, based on the determination of the number of operational cycles remaining, restrict the UAV from normal use. The one or more motors comprises a vertical or horizontal propulsion motor of the UAV.

In an embodiment, one or more computer-readable storage medium comprising a plurality of instructions to cause an apparatus, in response to execution by one or more processors of the apparatus, to receive sounds emanating from one or more motors included in an unmanned aerial vehicle (UAV) during operation of the one or more motors; predict a number of operational cycles remaining before the one or more motors is to fail based on analysis of the sounds; and, based on the determination of the number of operational cycles remaining, restrict the UAV from normal use. The one or more motors comprises a vertical or horizontal propulsion motor of the UAV.

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.