Processing

Please wait...

Settings

Settings

Goto Application

1. WO2020202857 - PREDICTIVE CLASSIFICATION OF FUTURE OPERATIONS

Publication Number WO/2020/202857
Publication Date 08.10.2020
International Application No. PCT/JP2020/006382
International Filing Date 04.02.2020
IPC
G05B 13/02 2006.01
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
13Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
02electric
G05B 13/04 2006.01
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
13Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
02electric
04involving the use of models or simulators
G05B 23/02 2006.01
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
23Testing or monitoring of control systems or parts thereof
02Electric testing or monitoring
G06N 3/04 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architecture, e.g. interconnection topology
CPC
G05B 23/024
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
23Testing or monitoring of control systems or parts thereof
02Electric testing or monitoring
0205by means of a monitoring system capable of detecting and responding to faults
0218characterised by the fault detection method dealing with either existing or incipient faults
0224Process history based detection method, e.g. whereby history implies the availability of large amounts of data
024Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
G05B 23/0254
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
23Testing or monitoring of control systems or parts thereof
02Electric testing or monitoring
0205by means of a monitoring system capable of detecting and responding to faults
0218characterised by the fault detection method dealing with either existing or incipient faults
0243model based detection method, e.g. first-principles knowledge model
0254based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
G05B 23/0283
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
23Testing or monitoring of control systems or parts thereof
02Electric testing or monitoring
0205by means of a monitoring system capable of detecting and responding to faults
0259characterized by the response to fault detection
0283Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]
G06N 3/0454
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0454using a combination of multiple neural nets
G06N 3/0481
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0481Non-linear activation functions, e.g. sigmoids, thresholds
Applicants
  • MITSUBISHI ELECTRIC CORPORATION [JP]/[JP]
Inventors
  • JHA, Devesh
  • ZHANG, Wenyu
  • LAFTCHIEV, Emil
  • NIKOVSKI, Daniel
Agents
  • FUKAMI PATENT OFFICE, P.C.
Priority Data
16/369,10929.03.2019US
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) PREDICTIVE CLASSIFICATION OF FUTURE OPERATIONS
(FR) CLASSIFICATION PRÉDICTIVE D'OPÉRATIONS FUTURES
Abstract
(EN)
A system evaluates a plurality of faults in an operation of a machine at a set of future instances of time. The system uses a neural network including a first subnetwork sequentially connected with a sequence of second subnetworks for each of the future instance of time such that an output of one subnetwork is an input to a subsequent subnetwork. The first subnetwork accepts the current time-series data and the current setpoints of operation of the machine. Each of the second subnetworks accepts the output of a preceding subnetwork, an internal state of the preceding subnetwork, and a future setpoint for a corresponding future instance of time. Each of the second subnetworks outputs an individual prediction of each fault of a plurality of faults at the corresponding future instance of time.
(FR)
La présente invention concerne un système qui évalue une pluralité de défauts dans une opération d'une machine au niveau d'un ensemble d'instances de temps futures. Le système utilise un réseau neuronal comprenant un premier sous-réseau connecté de manière séquentielle à une séquence de seconds sous-réseaux pour chaque élément de l'instance de temps future de telle sorte qu'une sortie d'un sous-réseau soit une entrée vers un sous-réseau ultérieur. Le premier sous-réseau accepte les données en série chronologique actuelles et les points de consigne actuels de l'opération de la machine. Chacun des seconds sous-réseaux accepte la sortie d'un sous-réseau précédent, un état interne du sous-réseau précédent, et un point de consigne futur pour une instance de temps future correspondante. Chacun des seconds sous-réseaux émet une prédiction individuelle de chaque défaut d'une pluralité de défauts à l'instance de temps future correspondante.
Latest bibliographic data on file with the International Bureau