A state estimation apparatus 1 includes an acquisition unit 2 that acquires deterioration information indicating a deterioration state of each structural object and a learning unit 3 that learns common information that is common between pieces of the deterioration information and estimation index information that is used for estimating a deterioration state of a target structural object, using the deterioration information as input.

A modular acoustic system for use to perform acoustic testing on an object across an entire audible spectrum with enhanced efficiency and acoustic capabilities is provided. The acoustic system includes an acoustic device having a housing to store a horn assembly with a plurality of transducers, an amplification and power distribution device with an amplifier electrically coupled to the horn assembly of the acoustic device, and a controller operably connected to the amplification and power distribution device. The controller is designed to transmit a plurality of noise signals over a network to the amplifier of the amplification and power distribution device to enable the plurality of transducers of the acoustic device to generate a plurality of acoustic waves across the entire audible spectrum.

A modular acoustic system for use to perform acoustic testing on an object across an entire audible spectrum with enhanced efficiency and acoustic capabilities is provided. The acoustic system includes an acoustic device having a housing to store a horn assembly with a plurality of transducers, an amplification and power distribution device with an amplifier electrically coupled to the horn assembly of the acoustic device, and a controller operably connected to the amplification and power distribution device. The controller is designed to transmit a plurality of noise signals over a network to the amplifier of the amplification and power distribution device to enable the plurality of transducers of the acoustic device to generate a plurality of acoustic waves across the entire audible spectrum.

A statistical method is used to separate periodic from non-periodic vibration peaks in machine vibration spectra. Generally, a machine vibration spectrum is not normally distributed because the amplitudes of periodic peaks are significantly large and random relative to the generally Gaussian noise. In a normally distributed signal, the statistical parameter Kurtosis has a value of 3. The method sequentially removes each largest amplitude peak from the peaks in a frequency region of interest in the spectrum until the Kurtosis has a value of three or less. The removed peaks, which are all considered to be periodic, are placed into a candidate peak list. As the process of building the candidate peak list proceeds, if the kurtosis of the remaining peaks in the frequency region of interest falls to three or less, the process stops and the candidate peak list is defined.

An allophone inspection device and inspection method thereof are provided. An allophone inspection device includes an array microphone unit in which a plurality of array microphones are disposed at predetermined intervals, and a controller configured to build reference data by quantifying analyzed allophone by collecting sound signals generated from surrounding based on a position where the array microphone unit is installed in advance and measure a surrounding sound signal through the array microphone unit to estimate whether or not noise is generated and a position of the sound source where the noise is generated based on the reference data.

The disclosure is directed to a method comprising the steps: carrying out multiple measurements on a mechanical object, the measurements each differing by one or more parameters influencing the measurement; determining a spectrogram on the basis of the measurement data of the measurements and depending on a predefined parameter of the mechanical object; determining one or more excitations of the mechanical object; reproducing the excitations in the spectrogram.

A method for diagnosing and predicting operation conditions of large-scale equipment based on feature fusion and conversion, including: collecting a vibration signal of each operating condition of the equipment, and establishing an original vibration acceleration data set of the vibration signal; performing noise reduction on the original vibration acceleration data set, and calculating a time domain parameter; performing EMD on a de-noised vibration acceleration and calculating a frequency domain parameter; constructing a training sample data set through the time domain parameter and the frequency domain parameter; establishing a GBDT model, and inputting the training sample data set into the GBDT model; extracting a leaf node number set from a trained GBDT model; performing one-hot encoding on the leaf node number set to obtain a sparse matrix; and inputting the sparse matrix into a factorization machine to obtain a prediction result.

A sampling device receives, from a transducer computing device located within a predefined proximity to an equipment in an operating environment, a vibration sample from the operating environment and increments a retrain counter. In response to determining that the incremented retrain counter does not meet or exceed a retrain threshold, the sampling device predicts, using a model, an anomalous or non-anomalous designation for the vibration sample and a cluster assignment, to a particular cluster of a set of clusters, for the vibration sample when the model predicts the non-anomalous designation for the vibration sample. The sampling device receives a subsequent vibration sample and further increments the retrain counter. In response to determining that the further incremented retrain counter exceeds a retrain threshold, the sampling device receives a subsequent set of vibration samples and retrains, using the subsequent vibration sample and the subsequent set of vibration samples, the model.

A moving part control system for a vehicle configured to cause a movement of a mechanical part for loosening the mechanical part from a stuck state to a loose state. The moving part control system includes an electrically controlled actuator device configured to cause a movement of the mechanical part, a processing circuitry configured to be operatively connected to the electrically controlled actuator device and configured to determine an amplitude and/or a frequency of an oscillating movement of the electrically controlled actuator device to determine if the mechanical part is in a stuck state or a loose state.

Accuracy of unsupervised anomalous sound detection is improved using a small number of pieces of anomalous sound data. A threshold deciding part (13) calculates an anomaly score for each of a plurality of pieces of anomalous sound data, using a normal model learned with normal sound data and an anomaly model expressing the pieces of anomalous sound data, and decides a minimum value among the anomaly scores as a threshold. A weight updating part (14) updates, using a plurality of pieces of normal sound data, the pieces of anomalous sound data and the threshold, weights of the anomaly model so that all the pieces of anomalous sound data are judged as anomalous, and probability of the pieces of normal sound data being judged as anomalous is minimized.