A data compression process reduces the amount of machine spectral data transmitted over a network while maintaining the details of spectral peaks used for machine health analysis. The data compression process also provides for the calculation of various types of spectral parameters, such as spectral band parameters, with negligible loss of accuracy.

A method for monitoring the operation of a machine that generates vibrations, includes a learning phase in which a knowledge base containing vibrational signatures representative of the operation of the machine is generated, and a monitoring phase in which the vibrations of the machine are compared to the knowledge base so as to detect an anomaly in the machine.

A method for automatically detecting the free vibration response segment of the high-speed railway bridges after trains passing. First, pre-select the test response sequence to be decomposed based on the maximum of the time instants corresponding to the absolute maximums of the response vectors at various measuring point. Then, Extract the single-frequency modal response from the test response by the iterative variational mode decomposition and fit the envelope amplitude of the modal response by Hilbert transform. Finally, the vibration features at each time instants are marked as decay vibration or non-decay vibration. The longest structural response segment that meets the decay vibration features is determined as the detected free vibration response segment for modal identification. This invention can effectively detect the free vibration data segment without human participation, which is of great significance for the real-time accurate modal analysis of high-speed railway bridges.

In feature value extraction processing step, feature values are extracted from time-series vibroacoustic data in equipment in operation. In machine learning diagnosis step, accumulated are feature values corresponding to normal equipment-operation-state and general measurement data on equipment. Probability distribution model related to correlation between general measurement data and feature values corresponding to normal equipment-operation-state and constructed through machine learning is fed with general-measurement data and feature values extracted in feature value extraction processing step to calculate degree of anomaly based on probability distribution model. In anomalous location estimation step, precalculated damaged-case dataset of degrees of anomaly is compared with actual-measurement dataset of degrees of anomaly calculated from general-measurement data in equipment to obtain degree of similarity for each location in the equipment and output the locations, in order ranked by degrees of similarity for each location in equipment, as locations having high likelihoods of being damaged.

A method of determining a noise or vibration response of a vehicle subassembly may include transmitting, via a controller, an input torque control signal to a first motor of a test apparatus. The first motor is mountable on a test fixture of the test apparatus and is configured to be coupled to the vehicle subassembly. The input torque control signal causes the first motor to provide an input torque characterized as a third derivative Gaussian function. The method further includes receiving a response of the vehicle subassembly to the input torque, and executing a control action with respect to the vehicle subassembly, via the controller, based on the response.

A diagnosis apparatus 1 includes: a generation unit 2 configured to acquire vibration information indicating vibration produced in a structure 20 from a plurality of sensors 21 provided to the structure 20, and to generate, using the vibration information, natural vibration mode information indicating a natural vibration mode shape; an occurrence rate calculation unit 3 configured to calculate a rate of occurrence of a normal natural vibration mode shape based on the number of times vibration was applied to the structure 20 and the number of times the normal natural vibration mode shape was generated when the vibration was applied; and a diagnosis unit 4 configured to diagnose whether or not repair and reinforcement performed on the structure were effective based on the rate of occurrence and a reference value.

Provided is an optical fiber sensor that is capable of highly accurately detecting an abnormality in a structure from vibration information. This optical fiber sensor includes an optical fiber that is laid in the vicinity of a structure, a light source for introducing pulsed light of a specific period into the optical fiber, and an optical sensor for detecting return light that has been obtained as a result of the introduction of the pulsed light into the optical fiber. The structure is determined to have an abnormality if the spectral centroid of vibration information exceeds a threshold.

A vibration signal-based smartwatch authentication method includes generating incremental vibration signals using a vibration motor in a smartwatch; performing frequency band-based hierarchical endpoint segmentation to obtain vibration signals at a plurality of frequency bands; extracting frequency-domain features for the vibration signals at the plurality of frequency bands; training a dynamic time warping model by taking the vibration signals at the plurality of frequency bands as a training data set, training a nearest neighbor model by taking the extracted frequency-domain features as training data; collecting to-be-authenticated vibration signals which are processed to serve as test data signals; discriminating similarities between the test data signals and corresponding training data signals through the dynamic time warping model, giving a classification result through the nearest neighbor model, performing weighted calculation on a discrimination result of the dynamic time warping model and a discrimination result of the nearest neighbor model to obtain an authentication result.

An information processing apparatus includes circuitry to obtain detected information representing a detected vibration generated in processing on a workpiece, and detect, based on the detected information, a start timing at which a punch has started striking the workpiece and a cracking timing at which the punch has cracked the workpiece.

A vibration analysis system includes: a signal input portion that receives an input of a vibration signal detected by a sensor; an intensity calculation portion that calculates a plurality of signal intensities corresponding to a plurality of frequency bands by analyzing the vibration signal; a first distance calculation portion that calculates a first Mahalanobis distance of a first signal space configured of the plurality of signal intensities with respect to a first unit space; a gravity center calculation portion that calculates two-dimensional gravity center data indicating gravity center positions of the plurality of signal intensities; a second distance calculation portion that calculates a second Mahalanobis distance of a second signal space configured of the gravity center data with respect to a second unit space; and an abnormality prediction portion that predicts an abnormality generation period of the object based on the first Mahalanobis distance and the second Mahalanobis distance.