Li et al., 2019 - Google Patents
A deep learning driven method for fault classification and degradation assessment in mechanical equipmentLi et al., 2019
- Document ID
- 15619574390209316540
- Author
- Li Z
- Wang Y
- Wang K
- Publication year
- Publication venue
- Computers in industry
External Links
Snippet
Mechanical degradation may cause equipment to break down with serious safety, environment, and economic impact. Since the equipment usually operates under a tough working environment, which makes it vulnerable and increases the complexity of fault …
- 230000015556 catabolic process 0 title abstract description 91
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
- G05B23/024—Quantitative 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
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
- G05B23/0227—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
- G05B23/0229—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions knowledge based, e.g. expert systems; genetic algorithms
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0283—Predictive 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]
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0275—Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
- G05B23/0278—Qualitative, e.g. if-then rules; Fuzzy logic; Lookup tables; Symptomatic search; FMEA
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
- G06N5/025—Extracting rules from data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Li et al. | A deep learning driven method for fault classification and degradation assessment in mechanical equipment | |
Li et al. | Data alignments in machinery remaining useful life prediction using deep adversarial neural networks | |
Brito et al. | An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery | |
Souza et al. | Deep learning for diagnosis and classification of faults in industrial rotating machinery | |
Pashazadeh et al. | Data driven sensor and actuator fault detection and isolation in wind turbine using classifier fusion | |
Zeng et al. | Gearbox oil temperature anomaly detection for wind turbine based on sparse Bayesian probability estimation | |
Wei et al. | A novel intelligent method for bearing fault diagnosis based on affinity propagation clustering and adaptive feature selection | |
Wen et al. | A new two-level hierarchical diagnosis network based on convolutional neural network | |
Lu et al. | An improved fault diagnosis method of rotating machinery using sensitive features and RLS-BP neural network | |
Attoui et al. | Novel predictive features using a wrapper model for rolling bearing fault diagnosis based on vibration signal analysis | |
Huo et al. | Incipient fault diagnosis of roller bearing using optimized wavelet transform based multi-speed vibration signatures | |
Purarjomandlangrudi et al. | A data mining approach for fault diagnosis: An application of anomaly detection algorithm | |
Li et al. | Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method | |
Yang et al. | An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring | |
Soualhi et al. | Pattern recognition method of fault diagnostics based on a new health indicator for smart manufacturing | |
Zheng et al. | Use of generalized refined composite multiscale fractional dispersion entropy to diagnose the faults of rolling bearing | |
Rai et al. | A novel health indicator based on the Lyapunov exponent, a probabilistic self-organizing map, and the Gini-Simpson index for calculating the RUL of bearings | |
Mosallam et al. | Time series trending for condition assessment and prognostics | |
Lu et al. | Joint training of a predictor network and a generative adversarial network for time series forecasting: A case study of bearing prognostics | |
Martínez-Rego et al. | Fault detection via recurrence time statistics and one-class classification | |
Zhou et al. | Conditional feature disentanglement learning for anomaly detection in machines operating under time-varying conditions | |
CN109034076A (en) | A kind of automatic clustering method and automatic cluster system of mechanical fault signals | |
Jin et al. | MD‐based approaches for system health monitoring: a review | |
Chen et al. | Explainable deep ensemble model for bearing fault diagnosis under variable conditions | |
Chen et al. | Interpretable fault diagnosis with shapelet temporal logic: Theory and application |