CN102226740A - Bearing fault detection method based on manner of controlling stochastic resonance by external periodic signal - Google Patents
Bearing fault detection method based on manner of controlling stochastic resonance by external periodic signal Download PDFInfo
- Publication number
- CN102226740A CN102226740A CN2011100961077A CN201110096107A CN102226740A CN 102226740 A CN102226740 A CN 102226740A CN 2011100961077 A CN2011100961077 A CN 2011100961077A CN 201110096107 A CN201110096107 A CN 201110096107A CN 102226740 A CN102226740 A CN 102226740A
- Authority
- CN
- China
- Prior art keywords
- signal
- frequency
- bearing fault
- fault
- bearing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Landscapes
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention discloses a bearing fault detection method based on a manner of controlling stochastic resonance by an external periodic signal. According to the method provided in the invention, after a bearing fault signal is converted by a variable metric method, the converted signal is input in a bistable system; meanwhile, an external single frequency periodic signal is taken as a control signal to act directly on the system; contact barrier height of the bistable system and an escape rate of Kramers are changed by continuously adjusting an amplitude of the control signal. Therefore, stochastic resonance can be generated or increased artificially; a spectral value of an output power spectrum at the position of an input signal frequency can be effectively improved; and thus a characteristic signal of a bearing fault can be detected accurately at last. The detection method provided in the invention enables the effective control of the stochastic resonance to be realized, thereby providing a novel method for early detection of equipment faults.
Description
Technical field
The present invention relates to a kind of fault-signal detection method, relate in particular to a kind of fault-signal detection method of in bearing failure diagnosis, using.
Background technology
Bearing is machine parts the most frequently used easy to wear.According to incompletely statistics, the fault of rotary machine about 30% is caused by bearing fault.The reason that produces bearing fault has fatigue flake, wearing and tearing, and plastic yield lures erosion, fracture, gummed, retainer damage etc.If can not in time diagnose the bearing initial failure, will make machinery and equipment produce catastrophic failure, thereby cause enormous economic loss.Therefore, the fault features of diagnosing out bearing guarantees that to avoiding the generation of catastrophic failure the normal operation of machinery and equipment has major and immediate significance.In the bearing failure diagnosis field, utilize the modern signal processing method that bearing fault is handled, from the signal that contains noise, accurately extract the fault signature signal, be one of research focus of current fault diagnosis.The method that adopts is utilized the difference on signal and the noisiness mostly, comes attenuating noise by the mathematic(al) manipulation method, extracts useful signal, does not have the physical mechanism of noise and signal energy conversion, thereby is difficult to amplify the weak signal in the very noisy.Secondly, the bearing fault signal is made up of fault signature signal and ground unrest.The lot of background noise can cause that the in-site measurement Signal-to-Noise reduces, and when serious interference, even can't detect the early sign of bearing fault, has influenced the normal operation of rotary machine.
Summary of the invention
The objective of the invention is at the deficiencies in the prior art, provide a kind of based on the bearing fault detection method that adds periodic signal control accidental resonance.
The objective of the invention is to be achieved through the following technical solutions: a kind of based on the bearing fault detection method that adds periodic signal control accidental resonance, it is characterized in that concrete steps are as follows:
(1) utilize acquisition system to gather vibration acceleration signal;
(2) the bearing fault signal is transformed to the small frequency signal through variable metric method;
(3) the bearing fault signal that will become behind the yardstick affacts bistable system, analyzes the power spectrum of bistable system output, recovers the collection yardstick of actual measurement bearing fault signal by the frequency compression scale ratio;
(4) add the single-frequency periodic signal and act on bistable system, regulate the amplitude of control signal, thereby produce artificially or the reinforcement accidental resonance, detect the bearing fault characteristic signal as control signal.
Further, described step (1) is specially: degree of will speed up sensor is fixed on the shaking table, and utilizing acquisition system to gather the bearing vibration acceleration signal is the bearing fault signal;
Further, described step (2) is specially: according to the frequency compression scale ratio
Definition compression sampling frequency
,
Actual samples frequency for fault-signal; Obtaining the numerical evaluation step-length by the compression sampling frequency is
, make that (the fault-signal characteristic frequency is for each frequency content of bearing fault signal
) by the frequency compression scale ratio
Linear compression, thereby the characteristic frequency boil down to of bearing fault signal
, make it to satisfy the condition of the theoretical medium and small frequency signal of the existing adiabatic approximation of accidental resonance.
Further, described step (3) is specially: the bearing fault signal that will become behind the yardstick affacts bistable system, by analyzing the power spectrum of bistable system output, catches the characteristic frequency of fault-signal, preferably by the frequency compression scale ratio
Recovering the fault-signal characteristic frequency is
Further, described step (4) is specially: add the single-frequency periodic signal
Act on bistable system as control signal, by regulating the amplitude of control signal
Bistable system potential barrier height and Kramers escape rate change, thereby can produce or strengthen accidental resonance artificially, strengthen the spectrum value of bistable system output power spectrum at the frequency input signal place effectively, realize the control of accidental resonance, detect the bearing fault characteristic signal.
The invention has the beneficial effects as follows, the present invention is by regulating the amplitude of control signal continuously, bistable system potential barrier height and Kramers escape rate change, thereby can produce or strengthen accidental resonance artificially, strengthened the spectrum value of bistable system output power spectrum effectively, finally can detect the bearing fault characteristic signal exactly at the frequency input signal place.The bearing fault signal can effectively amplify the fault signature signal by accidental resonance, improve the signal to noise ratio (S/N ratio) of fault signature signal, obtain the fault signature signal frequency exactly, this method has realized effective control of accidental resonance, for the equipment failure early detection provides a kind of new method.This method is applicable to that also other field relates to the Detection of Weak Signals in the very noisy, can widen the application of accidental resonance, has a good application prospect.
Description of drawings
Fig. 1 is the frequency detecting theory diagram that adds periodic signal control accidental resonance.
Fig. 2 is the bearing vibration signal power spectrum chart.
Fig. 3 is the bearing vibration signal power spectrum chart that contains noise.
Fig. 4 is
The time accidental resonance power spectrum chart.
Fig. 5 is
The time accidental resonance power spectrum chart.
Embodiment
The present invention is based on the bearing fault detection method that adds periodic signal control accidental resonance, concrete steps are as follows:
1, utilize acquisition system to gather vibration acceleration signal;
Degree of will speed up sensor is fixed on the shaking table, and utilizing acquisition system to gather the bearing vibration acceleration signal is the bearing fault signal.
2, the bearing fault signal is transformed to the small frequency signal through variable metric method;
According to the frequency compression scale ratio
Definition compression sampling frequency
, wherein,
Be the actual samples frequency of fault-signal,
Be the frequency compression scale ratio.Obtaining the numerical evaluation step-length by the compression sampling frequency is
, make that (the fault-signal characteristic frequency is for each frequency content of bearing fault signal
) by the frequency compression scale ratio
Linear compression, thereby the characteristic frequency boil down to of bearing fault signal
, make it to satisfy the condition of the theoretical medium and small frequency signal of the existing adiabatic approximation of accidental resonance.
3, the bearing fault signal that will become behind the yardstick affacts bistable system, analyzes the power spectrum of bistable system output, recovers the collection yardstick of actual measurement bearing fault signal by the frequency compression scale ratio;
Bearing fault signal behind the change yardstick is affacted bistable system, by analyzing the power spectrum of bistable system output, catch the characteristic frequency of fault-signal, preferably by the frequency compression scale ratio
Recovering the fault-signal characteristic frequency is
4, add the single-frequency periodic signal and act on bistable system, regulate the amplitude of control signal, thereby produce artificially or the reinforcement accidental resonance, detect the bearing fault characteristic signal as control signal;
Add the single-frequency periodic signal
Act on bistable system as control signal, by regulating the amplitude of control signal
Bistable system potential barrier height and Kramers escape rate change, thereby can produce or strengthen accidental resonance artificially, strengthen the spectrum value of bistable system output power spectrum at the frequency input signal place effectively, realize the control of accidental resonance, detect the bearing fault characteristic signal.
By the following examples content of the present invention is done further explanation.With this method the bearing fault signal is handled.Experimental data is by Case Western Reserve University(CWRU) provide.Fig. 2 is the power spectrum chart of the bearing vibration signal that records in the ecotopia of laboratory, and sample frequency is
, rotating speed is 1797rpm.Since often exist the generation of much noise and accidental resonance also to need suitable noise in the actual field environment, therefore, with noise intensity
White Gaussian noise as background noise be increased to and obtain mixed signal in the bearing vibration signal, power spectrum chart is as shown in Figure 3.As can be seen from Figure 3, there is no tangible fault characteristic information.The initialization system structural parameters
,
And do not exist and add periodic signal promptly
, the frequency compression scale ratio
, the compression sampling frequency is
The compressed scale ratio of mixed signal
Affact bistable system after the linear compression, the bistable system output power spectrum as shown in Figure 4.When adopting mode shown in Figure 1 to add parameter be
,
Single-frequency signals act on bistable system as control signal, then the bistable system output power spectrum is as shown in Figure 5.With Fig. 4 more as can be known, Fig. 5's
There is a tangible spectrum peak at the place, and is also original through dimensions in frequency
, this frequency is the frequency of fault signature signal, and its frequency theory value is
Claims (5)
1. one kind based on the bearing fault detection method that adds periodic signal control accidental resonance, it is characterized in that concrete steps are as follows:
(1) utilize acquisition system to gather vibration acceleration signal;
(2) the bearing fault signal is transformed to the small frequency signal through variable metric method;
(3) the bearing fault signal that will become behind the yardstick affacts bistable system, analyzes the power spectrum of bistable system output, recovers the collection yardstick of actual measurement bearing fault signal by the frequency compression scale ratio;
(4) add the single-frequency periodic signal and act on bistable system, regulate the amplitude of control signal, thereby produce artificially or the reinforcement accidental resonance, detect the bearing fault characteristic signal as control signal.
2. according to claim 1 based on the bearing fault detection method that adds periodic signal control accidental resonance, it is characterized in that, described step (1) is specially: degree of will speed up sensor is fixed on the shaking table, and utilizing acquisition system to gather the bearing vibration acceleration signal is the bearing fault signal.
3. according to claim 1 based on the bearing fault detection method that adds periodic signal control accidental resonance, it is characterized in that described step (2) is specially: according to the frequency compression scale ratio
Definition compression sampling frequency
,
Actual samples frequency for fault-signal; Obtaining the numerical evaluation step-length by the compression sampling frequency is
, make that (the fault-signal characteristic frequency is for each frequency content of bearing fault signal
) by the frequency compression scale ratio
Linear compression, thereby the characteristic frequency boil down to of bearing fault signal
, make it to satisfy the condition of the theoretical medium and small frequency signal of the existing adiabatic approximation of accidental resonance.
4. according to claim 1 a kind of based on the bearing fault detection method that adds periodic signal control accidental resonance, it is characterized in that, described step (3) is specially: the bearing fault signal that will become behind the yardstick affacts bistable system, by analyzing the power spectrum of bistable system output, catch the characteristic frequency of fault-signal, preferably by the frequency compression scale ratio
Recovering the fault-signal characteristic frequency is
5. according to claim 1 a kind of based on the bearing fault detection method that adds periodic signal control accidental resonance, it is characterized in that described step (4) is specially: add the single-frequency periodic signal
Act on bistable system as control signal, by regulating the amplitude of control signal
Bistable system potential barrier height and Kramers escape rate change, thereby can produce or strengthen accidental resonance artificially, strengthen the spectrum value of bistable system output power spectrum at the frequency input signal place effectively, realize the control of accidental resonance, detect the bearing fault characteristic signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201110096107 CN102226740B (en) | 2011-04-18 | 2011-04-18 | Bearing fault detection method based on manner of controlling stochastic resonance by external periodic signal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201110096107 CN102226740B (en) | 2011-04-18 | 2011-04-18 | Bearing fault detection method based on manner of controlling stochastic resonance by external periodic signal |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102226740A true CN102226740A (en) | 2011-10-26 |
| CN102226740B CN102226740B (en) | 2013-05-22 |
Family
ID=44807726
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 201110096107 Expired - Fee Related CN102226740B (en) | 2011-04-18 | 2011-04-18 | Bearing fault detection method based on manner of controlling stochastic resonance by external periodic signal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102226740B (en) |
Cited By (87)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013184688A1 (en) * | 2012-06-04 | 2013-12-12 | Brain Corporation | Stochastic apparatus and methods for implementing generalized learning rules |
| WO2014028855A1 (en) * | 2012-08-17 | 2014-02-20 | Brain Corporation | Apparatus and methods for spiking neuron network learning |
| US8793205B1 (en) | 2012-09-20 | 2014-07-29 | Brain Corporation | Robotic learning and evolution apparatus |
| US8983216B2 (en) | 2010-03-26 | 2015-03-17 | Brain Corporation | Invariant pulse latency coding systems and methods |
| US8990133B1 (en) | 2012-12-20 | 2015-03-24 | Brain Corporation | Apparatus and methods for state-dependent learning in spiking neuron networks |
| US8996177B2 (en) | 2013-03-15 | 2015-03-31 | Brain Corporation | Robotic training apparatus and methods |
| CN104483127A (en) * | 2014-10-22 | 2015-04-01 | 徐州隆安光电科技有限公司 | Method for extracting weak fault characteristic information of planetary gear |
| US9008840B1 (en) | 2013-04-19 | 2015-04-14 | Brain Corporation | Apparatus and methods for reinforcement-guided supervised learning |
| US9015092B2 (en) | 2012-06-04 | 2015-04-21 | Brain Corporation | Dynamically reconfigurable stochastic learning apparatus and methods |
| US9014416B1 (en) | 2012-06-29 | 2015-04-21 | Brain Corporation | Sensory processing apparatus and methods |
| US9047568B1 (en) | 2012-09-20 | 2015-06-02 | Brain Corporation | Apparatus and methods for encoding of sensory data using artificial spiking neurons |
| US9070039B2 (en) | 2013-02-01 | 2015-06-30 | Brian Corporation | Temporal winner takes all spiking neuron network sensory processing apparatus and methods |
| US9082079B1 (en) | 2012-10-22 | 2015-07-14 | Brain Corporation | Proportional-integral-derivative controller effecting expansion kernels comprising a plurality of spiking neurons associated with a plurality of receptive fields |
| US9092738B2 (en) | 2011-09-21 | 2015-07-28 | Qualcomm Technologies Inc. | Apparatus and methods for event-triggered updates in parallel networks |
| US9098811B2 (en) | 2012-06-04 | 2015-08-04 | Brain Corporation | Spiking neuron network apparatus and methods |
| US9104973B2 (en) | 2011-09-21 | 2015-08-11 | Qualcomm Technologies Inc. | Elementary network description for neuromorphic systems with plurality of doublets wherein doublet events rules are executed in parallel |
| US9111226B2 (en) | 2012-10-25 | 2015-08-18 | Brain Corporation | Modulated plasticity apparatus and methods for spiking neuron network |
| US9117176B2 (en) | 2011-09-21 | 2015-08-25 | Qualcomm Technologies Inc. | Round-trip engineering apparatus and methods for neural networks |
| US9122994B2 (en) | 2010-03-26 | 2015-09-01 | Brain Corporation | Apparatus and methods for temporally proximate object recognition |
| US9123127B2 (en) | 2012-12-10 | 2015-09-01 | Brain Corporation | Contrast enhancement spiking neuron network sensory processing apparatus and methods |
| US9129221B2 (en) | 2012-05-07 | 2015-09-08 | Brain Corporation | Spiking neural network feedback apparatus and methods |
| US9147156B2 (en) | 2011-09-21 | 2015-09-29 | Qualcomm Technologies Inc. | Apparatus and methods for synaptic update in a pulse-coded network |
| US9146546B2 (en) | 2012-06-04 | 2015-09-29 | Brain Corporation | Systems and apparatus for implementing task-specific learning using spiking neurons |
| US9152915B1 (en) | 2010-08-26 | 2015-10-06 | Brain Corporation | Apparatus and methods for encoding vector into pulse-code output |
| US9156165B2 (en) | 2011-09-21 | 2015-10-13 | Brain Corporation | Adaptive critic apparatus and methods |
| US9165245B2 (en) | 2011-09-21 | 2015-10-20 | Qualcomm Technologies Inc. | Apparatus and method for partial evaluation of synaptic updates based on system events |
| US9183493B2 (en) | 2012-10-25 | 2015-11-10 | Brain Corporation | Adaptive plasticity apparatus and methods for spiking neuron network |
| US9189730B1 (en) | 2012-09-20 | 2015-11-17 | Brain Corporation | Modulated stochasticity spiking neuron network controller apparatus and methods |
| US9195934B1 (en) | 2013-01-31 | 2015-11-24 | Brain Corporation | Spiking neuron classifier apparatus and methods using conditionally independent subsets |
| US9213937B2 (en) | 2011-09-21 | 2015-12-15 | Brain Corporation | Apparatus and methods for gating analog and spiking signals in artificial neural networks |
| US9218563B2 (en) | 2012-10-25 | 2015-12-22 | Brain Corporation | Spiking neuron sensory processing apparatus and methods for saliency detection |
| US9224090B2 (en) | 2012-05-07 | 2015-12-29 | Brain Corporation | Sensory input processing apparatus in a spiking neural network |
| US9239985B2 (en) | 2013-06-19 | 2016-01-19 | Brain Corporation | Apparatus and methods for processing inputs in an artificial neuron network |
| US9256215B2 (en) | 2012-07-27 | 2016-02-09 | Brain Corporation | Apparatus and methods for generalized state-dependent learning in spiking neuron networks |
| US9256823B2 (en) | 2012-07-27 | 2016-02-09 | Qualcomm Technologies Inc. | Apparatus and methods for efficient updates in spiking neuron network |
| US9275326B2 (en) | 2012-11-30 | 2016-03-01 | Brain Corporation | Rate stabilization through plasticity in spiking neuron network |
| US9311593B2 (en) | 2010-03-26 | 2016-04-12 | Brain Corporation | Apparatus and methods for polychronous encoding and multiplexing in neuronal prosthetic devices |
| US9311594B1 (en) | 2012-09-20 | 2016-04-12 | Brain Corporation | Spiking neuron network apparatus and methods for encoding of sensory data |
| US9311596B2 (en) | 2011-09-21 | 2016-04-12 | Qualcomm Technologies Inc. | Methods for memory management in parallel networks |
| US9346167B2 (en) | 2014-04-29 | 2016-05-24 | Brain Corporation | Trainable convolutional network apparatus and methods for operating a robotic vehicle |
| US9367798B2 (en) | 2012-09-20 | 2016-06-14 | Brain Corporation | Spiking neuron network adaptive control apparatus and methods |
| US9364950B2 (en) | 2014-03-13 | 2016-06-14 | Brain Corporation | Trainable modular robotic methods |
| US9405975B2 (en) | 2010-03-26 | 2016-08-02 | Brain Corporation | Apparatus and methods for pulse-code invariant object recognition |
| US9412064B2 (en) | 2011-08-17 | 2016-08-09 | Qualcomm Technologies Inc. | Event-based communication in spiking neuron networks communicating a neural activity payload with an efficacy update |
| US9426946B2 (en) | 2014-12-02 | 2016-08-30 | Brain Corporation | Computerized learning landscaping apparatus and methods |
| US9436909B2 (en) | 2013-06-19 | 2016-09-06 | Brain Corporation | Increased dynamic range artificial neuron network apparatus and methods |
| US9440352B2 (en) | 2012-08-31 | 2016-09-13 | Qualcomm Technologies Inc. | Apparatus and methods for robotic learning |
| US9446515B1 (en) | 2012-08-31 | 2016-09-20 | Brain Corporation | Apparatus and methods for controlling attention of a robot |
| US9489623B1 (en) | 2013-10-15 | 2016-11-08 | Brain Corporation | Apparatus and methods for backward propagation of errors in a spiking neuron network |
| US9533413B2 (en) | 2014-03-13 | 2017-01-03 | Brain Corporation | Trainable modular robotic apparatus and methods |
| US9552546B1 (en) | 2013-07-30 | 2017-01-24 | Brain Corporation | Apparatus and methods for efficacy balancing in a spiking neuron network |
| US9566710B2 (en) | 2011-06-02 | 2017-02-14 | Brain Corporation | Apparatus and methods for operating robotic devices using selective state space training |
| US9579790B2 (en) | 2014-09-17 | 2017-02-28 | Brain Corporation | Apparatus and methods for removal of learned behaviors in robots |
| CN106525426A (en) * | 2016-12-06 | 2017-03-22 | 安徽大学 | Weak signal enhancement detection method based on complementary stochastic resonance filter |
| US9604359B1 (en) | 2014-10-02 | 2017-03-28 | Brain Corporation | Apparatus and methods for training path navigation by robots |
| US9613308B2 (en) | 2014-04-03 | 2017-04-04 | Brain Corporation | Spoofing remote control apparatus and methods |
| US9630317B2 (en) | 2014-04-03 | 2017-04-25 | Brain Corporation | Learning apparatus and methods for control of robotic devices via spoofing |
| CN106840281A (en) * | 2016-12-27 | 2017-06-13 | 中国计量大学 | A kind of vortex street frequency detection method based on class square wave feedforward control accidental resonance |
| US9713982B2 (en) | 2014-05-22 | 2017-07-25 | Brain Corporation | Apparatus and methods for robotic operation using video imagery |
| US9764468B2 (en) | 2013-03-15 | 2017-09-19 | Brain Corporation | Adaptive predictor apparatus and methods |
| CN107246912A (en) * | 2017-06-22 | 2017-10-13 | 西北工业大学 | A kind of marine riser vortex-induced vibration monitoring method based on accidental resonance |
| US9792546B2 (en) | 2013-06-14 | 2017-10-17 | Brain Corporation | Hierarchical robotic controller apparatus and methods |
| US9789605B2 (en) | 2014-02-03 | 2017-10-17 | Brain Corporation | Apparatus and methods for control of robot actions based on corrective user inputs |
| US9821470B2 (en) | 2014-09-17 | 2017-11-21 | Brain Corporation | Apparatus and methods for context determination using real time sensor data |
| US9821457B1 (en) | 2013-05-31 | 2017-11-21 | Brain Corporation | Adaptive robotic interface apparatus and methods |
| US9840003B2 (en) | 2015-06-24 | 2017-12-12 | Brain Corporation | Apparatus and methods for safe navigation of robotic devices |
| US9844873B2 (en) | 2013-11-01 | 2017-12-19 | Brain Corporation | Apparatus and methods for haptic training of robots |
| US9848112B2 (en) | 2014-07-01 | 2017-12-19 | Brain Corporation | Optical detection apparatus and methods |
| US9849588B2 (en) | 2014-09-17 | 2017-12-26 | Brain Corporation | Apparatus and methods for remotely controlling robotic devices |
| US9860077B2 (en) | 2014-09-17 | 2018-01-02 | Brain Corporation | Home animation apparatus and methods |
| US9870617B2 (en) | 2014-09-19 | 2018-01-16 | Brain Corporation | Apparatus and methods for saliency detection based on color occurrence analysis |
| US9881349B1 (en) | 2014-10-24 | 2018-01-30 | Gopro, Inc. | Apparatus and methods for computerized object identification |
| US9939253B2 (en) | 2014-05-22 | 2018-04-10 | Brain Corporation | Apparatus and methods for distance estimation using multiple image sensors |
| US9950426B2 (en) | 2013-06-14 | 2018-04-24 | Brain Corporation | Predictive robotic controller apparatus and methods |
| US9987743B2 (en) | 2014-03-13 | 2018-06-05 | Brain Corporation | Trainable modular robotic apparatus and methods |
| US10057593B2 (en) | 2014-07-08 | 2018-08-21 | Brain Corporation | Apparatus and methods for distance estimation using stereo imagery |
| US10194163B2 (en) | 2014-05-22 | 2019-01-29 | Brain Corporation | Apparatus and methods for real time estimation of differential motion in live video |
| US10197664B2 (en) | 2015-07-20 | 2019-02-05 | Brain Corporation | Apparatus and methods for detection of objects using broadband signals |
| US10210452B2 (en) | 2011-09-21 | 2019-02-19 | Qualcomm Incorporated | High level neuromorphic network description apparatus and methods |
| CN109766874A (en) * | 2019-02-02 | 2019-05-17 | 王卓然 | A kind of fan trouble classifying identification method based on deep learning algorithm |
| US10295972B2 (en) | 2016-04-29 | 2019-05-21 | Brain Corporation | Systems and methods to operate controllable devices with gestures and/or noises |
| US10376117B2 (en) | 2015-02-26 | 2019-08-13 | Brain Corporation | Apparatus and methods for programming and training of robotic household appliances |
| CN110196173A (en) * | 2019-05-05 | 2019-09-03 | 成都大学 | A kind of strong noise environment machine operation troubles online test method based on self study |
| CN110763465A (en) * | 2019-10-22 | 2020-02-07 | 中国计量大学 | Bearing early fault signal detection system based on tristable characteristic with damping |
| CN112747926A (en) * | 2020-12-22 | 2021-05-04 | 电子科技大学 | Motor rolling bearing fault diagnosis method based on second-order parameter matching stochastic resonance |
| US11042775B1 (en) | 2013-02-08 | 2021-06-22 | Brain Corporation | Apparatus and methods for temporal proximity detection |
| US11831955B2 (en) | 2010-07-12 | 2023-11-28 | Time Warner Cable Enterprises Llc | Apparatus and methods for content management and account linking across multiple content delivery networks |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9384443B2 (en) | 2013-06-14 | 2016-07-05 | Brain Corporation | Robotic training apparatus and methods |
| US9579789B2 (en) | 2013-09-27 | 2017-02-28 | Brain Corporation | Apparatus and methods for training of robotic control arbitration |
| US9296101B2 (en) | 2013-09-27 | 2016-03-29 | Brain Corporation | Robotic control arbitration apparatus and methods |
| US9463571B2 (en) | 2013-11-01 | 2016-10-11 | Brian Corporation | Apparatus and methods for online training of robots |
| US9248569B2 (en) | 2013-11-22 | 2016-02-02 | Brain Corporation | Discrepancy detection apparatus and methods for machine learning |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006105727A (en) * | 2004-10-04 | 2006-04-20 | Nsk Ltd | Abnormality detecting unit of machine |
| CN101539472A (en) * | 2009-04-30 | 2009-09-23 | 北京工业大学 | Weak fault parallel-connected random resonance detection method of low-speed heave-load device |
| CN201707202U (en) * | 2010-05-24 | 2011-01-12 | 中国计量学院 | Performance detector for static pressure gas bearings |
-
2011
- 2011-04-18 CN CN 201110096107 patent/CN102226740B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006105727A (en) * | 2004-10-04 | 2006-04-20 | Nsk Ltd | Abnormality detecting unit of machine |
| CN101539472A (en) * | 2009-04-30 | 2009-09-23 | 北京工业大学 | Weak fault parallel-connected random resonance detection method of low-speed heave-load device |
| CN201707202U (en) * | 2010-05-24 | 2011-01-12 | 中国计量学院 | Performance detector for static pressure gas bearings |
Non-Patent Citations (1)
| Title |
|---|
| 赵艳菊，王太勇，冷永刚，徐跃，张攀: "级联双稳随机共振降噪下的经验模式分解", 《天津大学学报》 * |
Cited By (115)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9311593B2 (en) | 2010-03-26 | 2016-04-12 | Brain Corporation | Apparatus and methods for polychronous encoding and multiplexing in neuronal prosthetic devices |
| US9122994B2 (en) | 2010-03-26 | 2015-09-01 | Brain Corporation | Apparatus and methods for temporally proximate object recognition |
| US8983216B2 (en) | 2010-03-26 | 2015-03-17 | Brain Corporation | Invariant pulse latency coding systems and methods |
| US9405975B2 (en) | 2010-03-26 | 2016-08-02 | Brain Corporation | Apparatus and methods for pulse-code invariant object recognition |
| US11831955B2 (en) | 2010-07-12 | 2023-11-28 | Time Warner Cable Enterprises Llc | Apparatus and methods for content management and account linking across multiple content delivery networks |
| US9152915B1 (en) | 2010-08-26 | 2015-10-06 | Brain Corporation | Apparatus and methods for encoding vector into pulse-code output |
| US9193075B1 (en) | 2010-08-26 | 2015-11-24 | Brain Corporation | Apparatus and methods for object detection via optical flow cancellation |
| US9566710B2 (en) | 2011-06-02 | 2017-02-14 | Brain Corporation | Apparatus and methods for operating robotic devices using selective state space training |
| US9412064B2 (en) | 2011-08-17 | 2016-08-09 | Qualcomm Technologies Inc. | Event-based communication in spiking neuron networks communicating a neural activity payload with an efficacy update |
| US9156165B2 (en) | 2011-09-21 | 2015-10-13 | Brain Corporation | Adaptive critic apparatus and methods |
| US9213937B2 (en) | 2011-09-21 | 2015-12-15 | Brain Corporation | Apparatus and methods for gating analog and spiking signals in artificial neural networks |
| US9311596B2 (en) | 2011-09-21 | 2016-04-12 | Qualcomm Technologies Inc. | Methods for memory management in parallel networks |
| US9092738B2 (en) | 2011-09-21 | 2015-07-28 | Qualcomm Technologies Inc. | Apparatus and methods for event-triggered updates in parallel networks |
| US9165245B2 (en) | 2011-09-21 | 2015-10-20 | Qualcomm Technologies Inc. | Apparatus and method for partial evaluation of synaptic updates based on system events |
| US9104973B2 (en) | 2011-09-21 | 2015-08-11 | Qualcomm Technologies Inc. | Elementary network description for neuromorphic systems with plurality of doublets wherein doublet events rules are executed in parallel |
| US9460387B2 (en) | 2011-09-21 | 2016-10-04 | Qualcomm Technologies Inc. | Apparatus and methods for implementing event-based updates in neuron networks |
| US9117176B2 (en) | 2011-09-21 | 2015-08-25 | Qualcomm Technologies Inc. | Round-trip engineering apparatus and methods for neural networks |
| US10210452B2 (en) | 2011-09-21 | 2019-02-19 | Qualcomm Incorporated | High level neuromorphic network description apparatus and methods |
| US9147156B2 (en) | 2011-09-21 | 2015-09-29 | Qualcomm Technologies Inc. | Apparatus and methods for synaptic update in a pulse-coded network |
| US9224090B2 (en) | 2012-05-07 | 2015-12-29 | Brain Corporation | Sensory input processing apparatus in a spiking neural network |
| US9129221B2 (en) | 2012-05-07 | 2015-09-08 | Brain Corporation | Spiking neural network feedback apparatus and methods |
| US9104186B2 (en) | 2012-06-04 | 2015-08-11 | Brain Corporation | Stochastic apparatus and methods for implementing generalized learning rules |
| US9015092B2 (en) | 2012-06-04 | 2015-04-21 | Brain Corporation | Dynamically reconfigurable stochastic learning apparatus and methods |
| US9146546B2 (en) | 2012-06-04 | 2015-09-29 | Brain Corporation | Systems and apparatus for implementing task-specific learning using spiking neurons |
| WO2013184688A1 (en) * | 2012-06-04 | 2013-12-12 | Brain Corporation | Stochastic apparatus and methods for implementing generalized learning rules |
| US9098811B2 (en) | 2012-06-04 | 2015-08-04 | Brain Corporation | Spiking neuron network apparatus and methods |
| US9014416B1 (en) | 2012-06-29 | 2015-04-21 | Brain Corporation | Sensory processing apparatus and methods |
| US9256215B2 (en) | 2012-07-27 | 2016-02-09 | Brain Corporation | Apparatus and methods for generalized state-dependent learning in spiking neuron networks |
| US9256823B2 (en) | 2012-07-27 | 2016-02-09 | Qualcomm Technologies Inc. | Apparatus and methods for efficient updates in spiking neuron network |
| WO2014028855A1 (en) * | 2012-08-17 | 2014-02-20 | Brain Corporation | Apparatus and methods for spiking neuron network learning |
| US10545074B2 (en) | 2012-08-31 | 2020-01-28 | Gopro, Inc. | Apparatus and methods for controlling attention of a robot |
| US11867599B2 (en) | 2012-08-31 | 2024-01-09 | Gopro, Inc. | Apparatus and methods for controlling attention of a robot |
| US11360003B2 (en) | 2012-08-31 | 2022-06-14 | Gopro, Inc. | Apparatus and methods for controlling attention of a robot |
| US9440352B2 (en) | 2012-08-31 | 2016-09-13 | Qualcomm Technologies Inc. | Apparatus and methods for robotic learning |
| US9446515B1 (en) | 2012-08-31 | 2016-09-20 | Brain Corporation | Apparatus and methods for controlling attention of a robot |
| US10213921B2 (en) | 2012-08-31 | 2019-02-26 | Gopro, Inc. | Apparatus and methods for controlling attention of a robot |
| US9367798B2 (en) | 2012-09-20 | 2016-06-14 | Brain Corporation | Spiking neuron network adaptive control apparatus and methods |
| US9189730B1 (en) | 2012-09-20 | 2015-11-17 | Brain Corporation | Modulated stochasticity spiking neuron network controller apparatus and methods |
| US9311594B1 (en) | 2012-09-20 | 2016-04-12 | Brain Corporation | Spiking neuron network apparatus and methods for encoding of sensory data |
| US9047568B1 (en) | 2012-09-20 | 2015-06-02 | Brain Corporation | Apparatus and methods for encoding of sensory data using artificial spiking neurons |
| US8793205B1 (en) | 2012-09-20 | 2014-07-29 | Brain Corporation | Robotic learning and evolution apparatus |
| US9082079B1 (en) | 2012-10-22 | 2015-07-14 | Brain Corporation | Proportional-integral-derivative controller effecting expansion kernels comprising a plurality of spiking neurons associated with a plurality of receptive fields |
| US9218563B2 (en) | 2012-10-25 | 2015-12-22 | Brain Corporation | Spiking neuron sensory processing apparatus and methods for saliency detection |
| US9183493B2 (en) | 2012-10-25 | 2015-11-10 | Brain Corporation | Adaptive plasticity apparatus and methods for spiking neuron network |
| US9111226B2 (en) | 2012-10-25 | 2015-08-18 | Brain Corporation | Modulated plasticity apparatus and methods for spiking neuron network |
| US9275326B2 (en) | 2012-11-30 | 2016-03-01 | Brain Corporation | Rate stabilization through plasticity in spiking neuron network |
| US9123127B2 (en) | 2012-12-10 | 2015-09-01 | Brain Corporation | Contrast enhancement spiking neuron network sensory processing apparatus and methods |
| US8990133B1 (en) | 2012-12-20 | 2015-03-24 | Brain Corporation | Apparatus and methods for state-dependent learning in spiking neuron networks |
| US9195934B1 (en) | 2013-01-31 | 2015-11-24 | Brain Corporation | Spiking neuron classifier apparatus and methods using conditionally independent subsets |
| US9070039B2 (en) | 2013-02-01 | 2015-06-30 | Brian Corporation | Temporal winner takes all spiking neuron network sensory processing apparatus and methods |
| US11042775B1 (en) | 2013-02-08 | 2021-06-22 | Brain Corporation | Apparatus and methods for temporal proximity detection |
| US10155310B2 (en) | 2013-03-15 | 2018-12-18 | Brain Corporation | Adaptive predictor apparatus and methods |
| US9764468B2 (en) | 2013-03-15 | 2017-09-19 | Brain Corporation | Adaptive predictor apparatus and methods |
| US8996177B2 (en) | 2013-03-15 | 2015-03-31 | Brain Corporation | Robotic training apparatus and methods |
| US9008840B1 (en) | 2013-04-19 | 2015-04-14 | Brain Corporation | Apparatus and methods for reinforcement-guided supervised learning |
| US9821457B1 (en) | 2013-05-31 | 2017-11-21 | Brain Corporation | Adaptive robotic interface apparatus and methods |
| US9792546B2 (en) | 2013-06-14 | 2017-10-17 | Brain Corporation | Hierarchical robotic controller apparatus and methods |
| US9950426B2 (en) | 2013-06-14 | 2018-04-24 | Brain Corporation | Predictive robotic controller apparatus and methods |
| US9436909B2 (en) | 2013-06-19 | 2016-09-06 | Brain Corporation | Increased dynamic range artificial neuron network apparatus and methods |
| US9239985B2 (en) | 2013-06-19 | 2016-01-19 | Brain Corporation | Apparatus and methods for processing inputs in an artificial neuron network |
| US9552546B1 (en) | 2013-07-30 | 2017-01-24 | Brain Corporation | Apparatus and methods for efficacy balancing in a spiking neuron network |
| US9489623B1 (en) | 2013-10-15 | 2016-11-08 | Brain Corporation | Apparatus and methods for backward propagation of errors in a spiking neuron network |
| US9844873B2 (en) | 2013-11-01 | 2017-12-19 | Brain Corporation | Apparatus and methods for haptic training of robots |
| US9789605B2 (en) | 2014-02-03 | 2017-10-17 | Brain Corporation | Apparatus and methods for control of robot actions based on corrective user inputs |
| US10322507B2 (en) | 2014-02-03 | 2019-06-18 | Brain Corporation | Apparatus and methods for control of robot actions based on corrective user inputs |
| US9987743B2 (en) | 2014-03-13 | 2018-06-05 | Brain Corporation | Trainable modular robotic apparatus and methods |
| US9862092B2 (en) | 2014-03-13 | 2018-01-09 | Brain Corporation | Interface for use with trainable modular robotic apparatus |
| US9364950B2 (en) | 2014-03-13 | 2016-06-14 | Brain Corporation | Trainable modular robotic methods |
| US9533413B2 (en) | 2014-03-13 | 2017-01-03 | Brain Corporation | Trainable modular robotic apparatus and methods |
| US10391628B2 (en) | 2014-03-13 | 2019-08-27 | Brain Corporation | Trainable modular robotic apparatus and methods |
| US10166675B2 (en) | 2014-03-13 | 2019-01-01 | Brain Corporation | Trainable modular robotic apparatus |
| US9613308B2 (en) | 2014-04-03 | 2017-04-04 | Brain Corporation | Spoofing remote control apparatus and methods |
| US9630317B2 (en) | 2014-04-03 | 2017-04-25 | Brain Corporation | Learning apparatus and methods for control of robotic devices via spoofing |
| US9346167B2 (en) | 2014-04-29 | 2016-05-24 | Brain Corporation | Trainable convolutional network apparatus and methods for operating a robotic vehicle |
| US10194163B2 (en) | 2014-05-22 | 2019-01-29 | Brain Corporation | Apparatus and methods for real time estimation of differential motion in live video |
| US9939253B2 (en) | 2014-05-22 | 2018-04-10 | Brain Corporation | Apparatus and methods for distance estimation using multiple image sensors |
| US9713982B2 (en) | 2014-05-22 | 2017-07-25 | Brain Corporation | Apparatus and methods for robotic operation using video imagery |
| US9848112B2 (en) | 2014-07-01 | 2017-12-19 | Brain Corporation | Optical detection apparatus and methods |
| US10057593B2 (en) | 2014-07-08 | 2018-08-21 | Brain Corporation | Apparatus and methods for distance estimation using stereo imagery |
| US9860077B2 (en) | 2014-09-17 | 2018-01-02 | Brain Corporation | Home animation apparatus and methods |
| US9821470B2 (en) | 2014-09-17 | 2017-11-21 | Brain Corporation | Apparatus and methods for context determination using real time sensor data |
| US9579790B2 (en) | 2014-09-17 | 2017-02-28 | Brain Corporation | Apparatus and methods for removal of learned behaviors in robots |
| US9849588B2 (en) | 2014-09-17 | 2017-12-26 | Brain Corporation | Apparatus and methods for remotely controlling robotic devices |
| US9870617B2 (en) | 2014-09-19 | 2018-01-16 | Brain Corporation | Apparatus and methods for saliency detection based on color occurrence analysis |
| US10268919B1 (en) | 2014-09-19 | 2019-04-23 | Brain Corporation | Methods and apparatus for tracking objects using saliency |
| US10055850B2 (en) | 2014-09-19 | 2018-08-21 | Brain Corporation | Salient features tracking apparatus and methods using visual initialization |
| US10032280B2 (en) | 2014-09-19 | 2018-07-24 | Brain Corporation | Apparatus and methods for tracking salient features |
| US9687984B2 (en) | 2014-10-02 | 2017-06-27 | Brain Corporation | Apparatus and methods for training of robots |
| US10105841B1 (en) | 2014-10-02 | 2018-10-23 | Brain Corporation | Apparatus and methods for programming and training of robotic devices |
| US10131052B1 (en) | 2014-10-02 | 2018-11-20 | Brain Corporation | Persistent predictor apparatus and methods for task switching |
| US9902062B2 (en) | 2014-10-02 | 2018-02-27 | Brain Corporation | Apparatus and methods for training path navigation by robots |
| US9630318B2 (en) | 2014-10-02 | 2017-04-25 | Brain Corporation | Feature detection apparatus and methods for training of robotic navigation |
| US9604359B1 (en) | 2014-10-02 | 2017-03-28 | Brain Corporation | Apparatus and methods for training path navigation by robots |
| CN104483127A (en) * | 2014-10-22 | 2015-04-01 | 徐州隆安光电科技有限公司 | Method for extracting weak fault characteristic information of planetary gear |
| CN104483127B (en) * | 2014-10-22 | 2017-12-29 | 徐州隆安光电科技有限公司 | A kind of planetary gear feature information of weak faults extracting method |
| US10580102B1 (en) | 2014-10-24 | 2020-03-03 | Gopro, Inc. | Apparatus and methods for computerized object identification |
| US11562458B2 (en) | 2014-10-24 | 2023-01-24 | Gopro, Inc. | Autonomous vehicle control method, system, and medium |
| US9881349B1 (en) | 2014-10-24 | 2018-01-30 | Gopro, Inc. | Apparatus and methods for computerized object identification |
| US9426946B2 (en) | 2014-12-02 | 2016-08-30 | Brain Corporation | Computerized learning landscaping apparatus and methods |
| US10376117B2 (en) | 2015-02-26 | 2019-08-13 | Brain Corporation | Apparatus and methods for programming and training of robotic household appliances |
| US9840003B2 (en) | 2015-06-24 | 2017-12-12 | Brain Corporation | Apparatus and methods for safe navigation of robotic devices |
| US9873196B2 (en) | 2015-06-24 | 2018-01-23 | Brain Corporation | Bistatic object detection apparatus and methods |
| US10807230B2 (en) | 2015-06-24 | 2020-10-20 | Brain Corporation | Bistatic object detection apparatus and methods |
| US10197664B2 (en) | 2015-07-20 | 2019-02-05 | Brain Corporation | Apparatus and methods for detection of objects using broadband signals |
| US10295972B2 (en) | 2016-04-29 | 2019-05-21 | Brain Corporation | Systems and methods to operate controllable devices with gestures and/or noises |
| CN106525426A (en) * | 2016-12-06 | 2017-03-22 | 安徽大学 | Weak signal enhancement detection method based on complementary stochastic resonance filter |
| CN106525426B (en) * | 2016-12-06 | 2018-02-09 | 安徽大学 | Weak signal enhancement detection method based on complementary stochastic resonance filter |
| CN106840281B (en) * | 2016-12-27 | 2019-05-14 | 中国计量大学 | A kind of vortex street frequency detection method based on class square wave feedforward control accidental resonance |
| CN106840281A (en) * | 2016-12-27 | 2017-06-13 | 中国计量大学 | A kind of vortex street frequency detection method based on class square wave feedforward control accidental resonance |
| CN107246912A (en) * | 2017-06-22 | 2017-10-13 | 西北工业大学 | A kind of marine riser vortex-induced vibration monitoring method based on accidental resonance |
| CN109766874A (en) * | 2019-02-02 | 2019-05-17 | 王卓然 | A kind of fan trouble classifying identification method based on deep learning algorithm |
| CN110196173A (en) * | 2019-05-05 | 2019-09-03 | 成都大学 | A kind of strong noise environment machine operation troubles online test method based on self study |
| CN110763465A (en) * | 2019-10-22 | 2020-02-07 | 中国计量大学 | Bearing early fault signal detection system based on tristable characteristic with damping |
| CN112747926A (en) * | 2020-12-22 | 2021-05-04 | 电子科技大学 | Motor rolling bearing fault diagnosis method based on second-order parameter matching stochastic resonance |
| CN112747926B (en) * | 2020-12-22 | 2022-04-15 | 电子科技大学 | Motor rolling bearing fault diagnosis method based on second-order parameter matching stochastic resonance |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102226740B (en) | 2013-05-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102226740B (en) | Bearing fault detection method based on manner of controlling stochastic resonance by external periodic signal | |
| Yoon et al. | On the use of a single piezoelectric strain sensor for wind turbine planetary gearbox fault diagnosis | |
| CN109883703B (en) | Fan bearing health monitoring and diagnosing method based on vibration signal coherent cepstrum analysis | |
| CN203629869U (en) | Water turbine cavitation monitoring and analyzing system | |
| CN103335844A (en) | Fault detection method for adaptive stochastic resonance bearing | |
| CN107101827B (en) | A kind of low-speed heavy-loaded gear crack fault online test method | |
| CN102645270A (en) | Intelligent dual-mode vibration sensor for rotary machinery | |
| CN103344432A (en) | Method for detecting fiber bragg grating acoustic emission of helicopter bearing fault | |
| CN103344430A (en) | Fault diagnosis method of gearbox | |
| CN101794138A (en) | Dynamic characteristic test and analysis system for numerical control machine tool | |
| CN103245913B (en) | The method and system of Generator Set sub-synchronous oscillation input and analysis | |
| CN105021706A (en) | Grinding wheel broken state early warning recognition device and method | |
| CN102758727B (en) | Wind turbine state monitoring and error diagnosis system and method integrated into control system | |
| CN109632291A (en) | A kind of Fault Diagnosis of Gear Case method based on polynary mode decomposition-transfer entropy | |
| Xu et al. | Caputo-Fabrizio fractional order derivative stochastic resonance enhanced by ADOF and its application in fault diagnosis of wind turbine drivetrain | |
| TW201319539A (en) | Measurement system for diagnosing ball screw preload loss by vibration signal and voiceprint signal | |
| Zheng et al. | Zero-phase filter-based adaptive Fourier decomposition and its application to fault diagnosis of rolling bearing | |
| CN103630604B (en) | The recognition methods of centrifugal compressor half-opened impeller crack fault | |
| CN103743585A (en) | Mechanical failure diagnosing method | |
| CN203011692U (en) | Portable fault diagnosing device for rolling bearing | |
| CN101691632B (en) | Method for rapidly judging peak | |
| Zhuang et al. | Rolling bearing fault diagnosis by aperiodic stochastic resonance under variable speed conditions | |
| CN114091544A (en) | TBM disc cutter abrasion identification system based on vibration signal and neural network | |
| CN208270065U (en) | A kind of practical gas turbine Vibration-Measuring System | |
| CN202083465U (en) | Order tracking device for reciprocating machinery in case of transient condition |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| C17 | Cessation of patent right | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20130522 Termination date: 20140418 |