公开(公告)号：US20240280635A1

公开(公告)日：2024-08-22

申请号：US18570157

申请日：2022-03-22

Applicant: Hitachi, Ltd.

Inventor： Takashi ENDO ,  Takeshi IWATA ,  Toshiaki ROKUNOHE ,  Hikaru KOYAMA ,  Kota DOHI

IPC: G01R31/327 ,  H04R3/00

CPC classification number: G01R31/327 ,  H04R3/00

Abstract: The acoustic diagnosis device extracts a second feature amount of an acoustic signal in which a delay of an acoustic signal collected by the microphone is corrected, the delay being generated from a relative distance between the circuit breaker and the microphone, during diagnosis of the circuit breaker, calculates a similarity between the second feature amount of the acoustic signal during the diagnosis and the first feature amount in the plurality of event models for each time, estimates the time at which the similarity becomes highest, as an occurrence timing of each event, and diagnoses the circuit breaker from a plurality of estimated occurrence timings.

公开(公告)号：US20190310158A1

公开(公告)日：2019-10-10

申请号：US16251580

申请日：2019-01-18

Applicant: HITACHI, LTD.

Inventor： Kazuo ONO ,  Yuudai KAMADA ,  Ryosuke FUJIWARA ,  Takashi ENDO

IPC: G01M3/24 ,  G06F17/14 ,  G06F17/15

Abstract: A monitoring system includes a plurality of sensor terminals installed for a target to be measured, a base station wirelessly communicating with the sensor terminals, and a calculator communicable with the base station. The sensor terminal includes a sensor element which acquires vibration information of a target to be measured, an arithmetic unit which performs an arithmetic operation for data including the vibration information of the sensor element, and a wireless communication unit. The arithmetic unit performs a reduction process for reducing an amount of data of the sensor element. The wireless communication unit transmits data processed by the arithmetic unit to the base station. After the transmitted data is received, the base station transmits it to the calculator. The calculator includes a signal processing unit. The signal processing unit complements the reduction-processed data to acquire vibration information, and averages the vibration information for a predetermined period of time.

公开(公告)号：US20200251127A1

公开(公告)日：2020-08-06

申请号：US16565820

申请日：2019-09-10

Applicant: Hitachi, Ltd.

Inventor： Yohei KAWAGUCHI ,  Takashi ENDO

IPC: G10L25/51 ,  G10L25/18 ,  G06N20/20

Abstract: An abnormal sound detection device includes a first learning unit that inputs a normal operating sound of the machine equipment, and registers a normal sound base spectrum from an amplitude spectrogram, a new sound/new vibration extraction unit that executes supervised nonnegative matrix factorization (NMF) using the normal sound base spectrum as teacher data on an acoustic signal input during diagnosis, and outputs a nonnegative matrix not allowed to be approximated with a low rank in the normal sound base spectrum, a second learning unit that extracts a new sound/new vibration component from the normal operating sound of the machine equipment, learns a normal sound model from the new sound/new vibration component, and registers the normal sound model, and an abnormality detection unit that computes a probability that the new sound/new vibration component extracted from the acoustic signal of the machine equipment during diagnosis is generated from the normal sound model.

公开(公告)号：US20220397894A1

公开(公告)日：2022-12-15

申请号：US17828714

申请日：2022-05-31

Applicant: Hitachi, Ltd.

Inventor： Kota DOHI ,  Harsh Pramodbhai PUROHIT ,  Ryo TANABE ,  Masaaki YAMAMOTO ,  Takashi ENDO ,  Yohei KAWAGUCHI

IPC: G05B23/02

Abstract: Provided are an abnormality detection system and an abnormality detection method capable of performing more stable abnormality detection. An abnormality detection system that detects an abnormality of the target machine by a computer includes a communication unit configured to acquire first data from a first sensor attached to the target machine and second data from a second sensor attached to the target machine, an arithmetic unit, and a memory unit. The arithmetic unit includes an encoding unit trained to generate latent expressions including a predetermined latent expression that estimates the second data on the basis of the first data, a decoding unit trained to restore the first data from the latent expressions, and an abnormality detection unit configured to detect the abnormality of the target machine on the basis of a restoration error between the first data and the first data restored by the decoding unit.

公开(公告)号：US20220208184A1

公开(公告)日：2022-06-30

申请号：US17469514

申请日：2021-09-08

Applicant: HITACHI, LTD.

Inventor： Harsh Pramodbhai PUROHIT ,  Takashi ENDO ,  Yohei KAWAGUCHI

IPC: G10L15/20 ,  G10L15/02 ,  G10L17/18 ,  G06N3/08

Abstract: An anomaly detection apparatus includes a device identification database that stores device identification information for identifying a specific device for each type of a device, a hierarchical conditional vector generation unit that generates a hierarchical conditional vector based on the device identification information, an extraction unit that extracts a target device feature amount vector indicating a feature amount of an acoustic signal acquired from a target device by analyzing the acoustic signal, a hierarchical condition adversarial neural network that outputs background noise level information indicating a background noise level of a surrounding environment of the target device and true/false determination information indicating true/false of the target device feature amount vector by analyzing the hierarchical conditional vector and the target device feature amount vector, and an anomaly determination unit that determines whether an anomaly exists in the target device feature amount vector.

公开(公告)号：US20220269988A1

公开(公告)日：2022-08-25

申请号：US17590464

申请日：2022-02-01

Applicant: Hitachi, Ltd.

Inventor： Kota DOHI ,  Takashi ENDO ,  Yohei KAWAGUCHI

IPC: G06N20/00 ,  G05B23/02 ,  G01M99/00

Abstract: An abnormality degree calculation system includes a concept classification assignment unit that assigns a predetermined concept classification based on an identification number of a target device, a feature value vector extraction unit that extracts a feature value vector based on sensor data of a sensor corresponding to the target device, a likelihood calculation unit that calculates a likelihood of the feature value vector by using a machine learning model obtained from a learning database, a loss calculation unit that calculates a loss using a loss function as a function of the likelihood, a model update unit that updates the model by using the loss and a model, a re-learning necessity determination unit that determines whether re-learning is necessary from the calculated likelihood when an abnormality of the target device is detected, and an abnormality degree calculation unit that calculates an abnormality degree when the re-learning is unnecessary.