Provided is a self-propelled inspection device capable of improving efficiency of system introduction cost, setting, and update work necessary for self-propelling in a self-propelled inspection device expected to be operated outdoors for a long period of time. Therefore, there is provided the self-propelled inspection device that autonomously inspects an inspection object while autonomously traveling an inspection route, the self-propelled inspection device including: a self-position estimation unit that estimates a self-position; a map information database that manages map information for autonomous traveling; a traveling unit including a drive mechanism and a steering mechanism; a sensor that senses the inspection object; a map information update unit that updates the map information based on information sensed by the sensor; and a traveling unit control unit that controls the traveling unit based on the updated map information.

Detecting and classifying damage to a vehicle. One example system includes a microphone positioned to detect sound waves inside the vehicle, one or more sensors positioned on the vehicle and configured to sense a characteristic of a windshield of the vehicle, and an electronic processor communicatively coupled to the one or more sensors and the microphone. The electronic processor is configured to receive sensor information from the one or more sensors and to receive an electrical signal from the microphone. The electronic processor is configured to determine, based on the sensor information, whether a crack event has occurred. The electronic processor is configured to, in response to determining that a crack event has occurred, determine a cause of the crack event based on the electrical signal received from the microphone. The electronic processor is configured to execute a mitigation action based on the cause of the crack event.

Detecting and classifying damage to a vehicle. One example system includes a microphone positioned to detect sound waves inside the vehicle, one or more sensors positioned on the vehicle and configured to sense a characteristic of a windshield of the vehicle, and an electronic processor communicatively coupled to the one or more sensors and the microphone. The electronic processor is configured to receive sensor information from the one or more sensors and to receive an electrical signal from the microphone. The electronic processor is configured to determine, based on the sensor information, whether a crack event has occurred. The electronic processor is configured to, in response to determining that a crack event has occurred, determine a cause of the crack event based on the electrical signal received from the microphone. The electronic processor is configured to execute a mitigation action based on the cause of the crack event.

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.

A machine learning device which learns fault prediction of one of a main shaft of a machine tool and a motor driving the main shaft, including a state observation unit observing a state variable including at least one of data output from a motor controller controlling the motor, data output from a detector detecting a state of the motor, and data output from a measuring device measuring a state of the one of the main shaft and the motor; a determination data obtaining unit obtaining determination data upon determining one of whether a fault has occurred in the one of the main shaft and the motor and a degree of fault; and a learning unit learning the fault prediction of the one of the main shaft and the motor in accordance with a data set generated based on a combination of the state variable and the determination data.

A machine learning device which learns fault prediction of one of a main shaft of a machine tool and a motor driving the main shaft, including a state observation unit observing a state variable including at least one of data output from a motor controller controlling the motor, data output from a detector detecting a state of the motor, and data output from a measuring device measuring a state of the one of the main shaft and the motor; a determination data obtaining unit obtaining determination data upon determining one of whether a fault has occurred in the one of the main shaft and the motor and a degree of fault; and a learning unit learning the fault prediction of the one of the main shaft and the motor in accordance with a data set generated based on a combination of the state variable and the determination data.

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.

According to one embodiment, a structure evaluation system of the embodiments includes a plurality of sensors, an arrival time determiner, a reliability calculator, and a map generator. The plurality of sensors detect elastic waves. The arrival time determiner determines arrival times of the elastic waves using elastic waves detected by the plurality of respective sensors. The reliability calculator calculates reliabilities related to measurement waveforms of the elastic waves on the basis of the arrival times. The map generator generates a first map on the basis of the calculated reliabilities or the reliabilities and a distance.

According to one embodiment, a structure evaluation system of the embodiments includes a plurality of sensors, an arrival time determiner, a reliability calculator, and a map generator. The plurality of sensors detect elastic waves. The arrival time determiner determines arrival times of the elastic waves using elastic waves detected by the plurality of respective sensors. The reliability calculator calculates reliabilities related to measurement waveforms of the elastic waves on the basis of the arrival times. The map generator generates a first map on the basis of the calculated reliabilities or the reliabilities and a distance.

Disclosed is a method for determining a whole macro-micro process of rock deformation and failure based on a four-parameter test, including following steps: firstly, obtaining acoustic emission data and deformation data of a sample in a compression test, and then calculating the deformation data according to a finite deformation theory to obtain a mean rotation angle θ at each stress level; using Grassberger-Procaccia (G-P) algorithm to calculate the acoustic emission data, and obtaining a fractal dimension of a temporal distribution D.sub.T of an acoustic emission signal and calculating a fractal dimension of a spatial distribution D.sub.S; obtaining a microscopic morphology of a fracture surface by scanning electron microscope (SEM) test after the compression test, and calculating a fractal dimension D.sub.A of the fracture surface; finally, obtaining a mathematical trend relationship between θ and D.sub.T, D.sub.S and D.sub.A according to a comprehensive analysis of D.sub.T, D.sub.S, D.sub.A and θ.