The present invention discloses a boom monitoring method and engineering machinery comprising a boom monitoring system. The method comprises obtaining a boom damage signal monitored in boom operation by a piezoelectric sensing network formed by a plurality of piezoelectric sensors arranged at different points on a boom, and determining a damage position of the boom and a corresponding first boom damage value such that when the first boom damage value reaches a preset starting value of an optical fiber sensing network formed by a plurality of optical fiber sensors arranged at the different monitoring points on the boom, optical wave values of the corresponding monitoring points are obtained and a boom crack signal is determined. A second boom damage value is calculated according to the boom crack signal, which comprises a crack change factor and a crack length. According to the present invention, the boom is monitored with improved efficiency.

An apparatus for monitoring mechanical integrity of an eye-safety component of an illuminator is disclosed. The apparatus comprises a transducer operable to create a vibration in the eye-safety component, a sensor operable to sense the vibration in the eye safety component and to output a signal representative of the sensed vibration, and a processor. The processor is operable to: monitor the signal from the sensor; determine if the signal comprises at least one parameter that falls outside of a pre-determined acceptable range, the pre-determined acceptable range being indicative of mechanical integrity of the eye-safety component; and initiate a safety action in response to a determination that the at least one parameter falls outside of the pre-determined acceptable range thereby indicating a loss of mechanical integrity.

An air-conditioning apparatus includes a drain pan that receives water, and a detector including an ultrasonic sensor that emits and receives ultrasonic waves. The detector analyzes a response signal from the ultrasonic sensor to detect a contaminant generated in the drain pan. A bottom flat surface portion that forms a bottom of the drain pan is located parallel to a flat surface portion that forms a receiving surface of the ultrasonic sensor.

This disclosure generally relates to the field of structural health monitoring, and, more particularly, to a method and system for evaluating residual life of components made of composite materials. Existing methods require performing computational methods such as Finite Element Analysis (FEA) on the results of Non-Destructive Testing (NDT) every time a component is inspected. This makes the process expensive and time-consuming. Thus, embodiments of present disclosure provide a method wherein NDT is performed using different sensing methods such as ultrasound, ultrasound pulse echo, thermography to determine type of defect, location of defect and depth of defect in a test component which are then fed into a pre-trained machine learning model to predict residual life of the component. Testing time is greatly reduced since the pre-trained machine learning model is trained offline using results of the computational methods.

A method is disclosed. In one example, the method includes bonding a first panel of a first material to a base panel in a first gas atmosphere, wherein multiple hermetically sealed first cavities encapsulating gas of the first gas atmosphere are formed between the first panel and the base panel. The method further includes bonding a second panel of a second material to at least one of the base panel and the first panel, wherein multiple second cavities are formed between the second panel and the at least one of the base panel and the first panel.

Described herein are systems and methods based on acoustic emission (AE) technology to monitor a concrete structure for a short interval and, based on signals acquired, estimate Alkali-silica reaction (ASR) progression status in the structure remotely and efficiently without halting any serviceability and operational activities of the structure, knowing the ASR progression status of the structure helps determine rehabilitation and future structural safety and serviceability of the structure.

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.

In accordance with a method for characterization of particles in a fluid-filled hollow structure, an ultrasound signal with a frequency spectrum, which exhibits a local maximum at a variable measurement frequency, is emitted in the direction of a part area of the hollow structure and reflected components are detected. The measurement frequency is tuned in a predetermined measurement interval, and depending on the detected reflected components, a spectral response curve is acquired as a function of the measurement frequency. Depending on the response curve, at least one characteristic property for a part of the particles located in the part area of the hollow structure is determined. The characteristic property includes a measure for an adhesion of the particles of the part of the particles located in the part area of the hollow structure.

A method for measuring health of a motor includes: (a) measuring, by an electrocardiogram sensor, vibration of the motor to obtain at least two electrical signals with each of the at least two electrical signals representing a harmonic of the vibration of the motor; (b) comparing each of the at least two electrical signals with a corresponding baseline; (c) based on the comparison, determining whether any one of the at least two electrical signals includes one or more artifacts wherein an artifact in a respective one of the at least two electrical signals is a deviation from a respective one of the corresponding baseline; and (d) based on any one of the at least two electrical signals including the one or more artifacts, providing an estimated time to failure for the motor.

Provided are a structure inspection method and a structure inspection system capable of efficiently inspecting structure and predicting deterioration with high accuracy. The structure inspection method includes: acquiring information on a location having internal damage within an inspection target region; and imaging the inspection target region with a visible light camera a plurality of times while shifting an imaging location, wherein a location except for the location having the internal damage is imaged with first pixel resolution and the location having internal damage is imaged with second pixel resolution higher than the first pixel resolution. Damage appearing on a surface of the structure is detected on the basis of a visible light image captured by the visible light camera. Information on the location having internal damage within the inspection target region is acquired by capturing an image that visualizes an internal state of the inspection target region.