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 ultrasonic quality control as disclosed can inspect a quality of a piece and classify the piece automatically. The piece can be scanned, and an image formed from the scanning. A reference piece is also scanned, and a reference image is formed. A negative image of the reference image is formed, and an indication image is created by utilizing the image and the negative image. The indication image is filtered by utilizing several image filters, each image filter filtering all data of the indication image except an image filter specific indication level data. Further several indication levels data are provided from the image filter specific indication level data, and the piece can be classified utilizing the several indication levels data.

A system and method to evaluate and/or classify non-destructive testing sensor data, the system and method including: a transmitter configured to provide energy to a material; one or more sensors configured to convert the energy returned from the material into sensor data; a receiver configured to receive sensor data; an attenuation inversion module configured to apply a mathematical transformation to the sensor data to provide transformed sensor data; an analysis module configured to process the transformed sensor data to provided processed sensor date, by: determining values from the transformed sensor data; applying mathematical transformations to the values to produce a set of single values that represent the sensor data; a classification module configured to classify the processed sensor data; and an output module configured to output the results of the classification.

A method of automatically inspecting a weld bead deposited in a plurality of passes in a chamfer formed between two parts by performing the following steps: positioning at least one emission electromagnetic acoustic sensor on one side of the chamfer and at least one reception electromagnetic acoustic sensor on an opposite side of the chamfer, the ultrasound wave emission sensor being configured to emit Rayleigh surface waves; while depositing a pass, automatically moving the sensors to follow the movement of welding electrodes along the chamfer; activating the sensors while they are moving to enable the emission sensor to generate and emit Rayleigh waves towards the pass of the weld bead that is being deposited, the reception sensor receiving the ultrasound signals transmitted and/or reflected in said pass; and reiterating the operation for the entire pass of the weld bead.

A method for detecting faults in plates includes the steps of: transmitting an acoustic signal towards the plate from a transmitting transducer, and receiving the acoustical signal from the plate in a receiving transducer. The receiving transducer is mounted at a distance from the transmitting transducer. The method includes the further steps of identifying zones of the plate wherein energy levels of the received signals are attenuated compared to other zones of the plate, and comparing the energy levels of the A.sub.2 and S.sub.3 guided Lamb modes in the received signals in the identified zones.

A non-destructive process of inspecting an object can include positioning a first and second ultrasonic element relative to the body of the object and offsetting the first and second ultrasonic elements in a direction orthogonal to a longitudinal axis of the body. A further process of inspecting an object can include creating a map of the body of the object including at least one anomaly and providing a quality value associated with the body based on evaluation of one or more criteria selected from the group consisting of the type, number, size, shape, position, orientation, edge sharpness, and any combination thereof of the at least one anomaly. An ultrasonic system can include a first and second ultrasonic element and a processing element. The process element can be configured to create a map of the body including at least one anomaly and provide a quality value associated with the body based on evaluation of one or more criteria selected from the group consisting of the type, number, size, shape, position, orientation, edge sharpness, and any combination thereof of the at least one anomaly.

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.

To provide an anomalous sound detection training technique by which a feature amount extraction function for detecting anomalous sound can be generated irrespective of whether training data for anomalous signals is available or not. An anomalous sound detection training apparatus includes: a first function updating unit 3 that updates a feature amount extraction function and an feature amount inverse transformation function, which are input, based on an optimization index of a variational autoencoder; an acoustic feature extraction unit 4 that extracts an acoustic feature of normal sound based on training data for normal sound; a normal sound model updating unit 5 that updates a normal sound model by using the acoustic feature that is extracted; a threshold updating unit 6 that obtains a threshold φ.sub.ρ corresponding to a false positive rate ρ, which has a predetermined value, by using the training data for normal sound and the feature amount extraction function that is input; and a second function updating unit 8 that updates the feature amount extraction function that is updated, based on a Neyman-Pearson-type optimization index defined by the threshold φ.sub.ρ that is obtained, and repeatedly performs processing of each of the above-mentioned units.

An ultrasonic inspection apparatus includes: an acquisition unit acquiring a signal indicating a fundamental wave and a second harmonic of an ultrasonic wave, which are obtained by the ultrasonic wave being scanned over an inspection object through a medium, at each scanning position; a calculation unit calculating a value obtained by dividing a second harmonic amplitude by a square of a fundamental wave amplitude, at each scanning position; and an output unit outputting information on a defect of the inspection object, based on the value obtained by dividing the second harmonic amplitude by the square of the fundamental wave amplitude.

Material waste screening is provided. A sensor obtains data related to an object. A processor classifies the object based on the data to identify a recycle category for the object, open a recycle bin for the identified recycle category, instruct the operator to deposit the object in the opened recycle bin, determine a level of compliance of the object, and create at least one new instruction to increase the level of compliance.