A device is configured to operate a Heating, Ventilation, and Air Conditioning (HVAC) system. The device is further configured to determine that the speed of a combustion air inducer has exceeded a speed threshold value. The device is further configured to receive an audio signal from a microphone while operating the HVAC system, to identify an audio signature for the combustion air inducer from an audio signature library, and to determine the audio signature for the combustion air inducer is present within the audio signal. The device is further configured to determine a fault type based on the determination that the audio signature for the combustion air inducer is present within the audio signal, to identify a component identifier for a component of the HVAC system that is associated with fault type, and to output a recommendation identifying the component identifier.

An embodiment of the present disclosure may provide a method of detecting a fault location using an acoustic emission signal, including a measuring step of measuring, by a signal measuring unit including at least three sensors disposed in a diagnosed subject and isolated from one another, an acoustic emission signal generated from a faulty part of the diagnosed subject, a signal pre-processing step of filtering and amplifying, by the signal pre-processing unit, the acoustic emission signal, an extraction step of extracting, by a data operation unit, a measuring time, that is, the time when the acoustic emission signal reaches each of the at least three sensors of the signal measuring unit, and a first analysis step of analyzing, by a data analysis unit, a location and occurrence time of the faulty part by using the measuring time and location information of the signal measuring unit.

Systems and methods are disclosed for detecting a crack in an automotive windshield and alerting a user of the same. This can allow the user to repair the crack before the user might otherwise detect the crack by his/her own visual inspection. The windshield can be provided with emitters configured to emit signals (e.g., sound, light, etc.) and corresponding detectors configured to detect the emitted signals. Signal profiles or signatures can be stored that represent normal measurements when there is no crack. Upon detecting a signal signature that deviates from the stored normal signal signatures, the system can notify the user of a potential crack in the windshield. The system can also determine the location of the crack based upon which of the detectors detect a change in the detected signal.

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.

Embodiments provide systems and methods for utilizing acoustic sensors to detect defects via online or in situ monitoring of additive manufacturing (AM) processes. Sensors may capture acoustic waves associated with AM manufacturing operations. The acoustic emissions in combination with other sensing data, such as cameras or thermometers, may be used to characterize the state of the AM process, such as to detect a defect has occurred or confirm a defect has not occurred. When defects are detected, the AM process may be stopped to prevent further processing of a defective part. When defects are predicted as likely to occur, operational parameters of the AM device or process may be adjusted to mitigate the occurrence of a defect. The techniques disclosed herein enable detection of defects that occur underneath the surface of the part being manufactured, as well as correct issues with the AM device or process before a defect occurs.

Embodiments provide systems and methods for utilizing acoustic sensors to detect defects via online or in situ monitoring of additive manufacturing (AM) processes. Sensors may capture acoustic waves associated with AM manufacturing operations. The acoustic emissions in combination with other sensing data, such as cameras or thermometers, may be used to characterize the state of the AM process, such as to detect a defect has occurred or confirm a defect has not occurred. When defects are detected, the AM process may be stopped to prevent further processing of a defective part. When defects are predicted as likely to occur, operational parameters of the AM device or process may be adjusted to mitigate the occurrence of a defect. The techniques disclosed herein enable detection of defects that occur underneath the surface of the part being manufactured, as well as correct issues with the AM device or process before a defect occurs.

An acoustic garbage classification method using a one-dimensional convolutional neural network (1D-CNN) is provided. The method includes: acquiring sound signals generated by falling garbage; preprocessing the sound signals; acquiring and preprocessing the sound signals of different types of garbage, building a sound database for garbage classification, and establishing and training a 1D-CNN model; acquiring a sound signal of garbage to be classified, and inputting the sound signal into the trained 1D-CNN for garbage classification to obtain a classification result. The present disclosure is helpful to assist people in accurate garbage classification, improves the accuracy of garbage classification and recycling, and has high practical and popularization value.

An ultrasonic flowmeter having a measuring tube, a control unit, at least one first ultrasonic measuring unit and a second ultrasonic measuring unit, the measuring tube having a measuring tube interior and a measuring tube longitudinal axis, wherein each of the ultrasonic measuring units is arranged on the measuring tube, wherein each ultrasonic measuring unit has a first ultrasonic transducer and a second ultrasonic transducer, the first and the second ultrasonic transducers spanning a sound measuring section with a sound axis. The sound measuring section and the sound axis penetrate the measuring tube interior for carrying out ultrasonic measurements. To provide an ultrasonic flowmeter for reliable measurement of a multi-phase medium, the sound axis of the first ultrasonic measuring unit and the sound axis of the second ultrasonic measuring unit span a sound measuring plane which extends substantially parallel to the longitudinal axis of the measuring tube.

Methods, systems, and computer-readable storage media for performing high-resolution assessment of the condition of pipes of a fluid distribution system using in-bracket excitation. Acoustical impulses are generated in a pipe at two excitation locations along the pipe while signal data is recorded from two acoustic sensors, at least one of the excitation locations being located in-bracket of the two acoustic sensors. A first time delay between the arrival of the acoustical impulses at the two acoustic sensors is computed from the signal data recorded during generation of the impulses at the first excitation location, and a second time delay between the arrival of the impulses at the two sensors is computed from the signal data recorded during generation of the impulses at the second excitation location. An acoustic propagation velocity is computed for a section of the pipe defined by the first and second excitation location based on the first time delay, the second time delay, and a distance between the excitation locations, and a condition of the section of pipe is determined from the computed acoustic propagation velocity.