CN102226740B - 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
- CN102226740B CN102226740B CN 201110096107 CN201110096107A CN102226740B CN 102226740 B CN102226740 B CN 102226740B CN 201110096107 CN201110096107 CN 201110096107 CN 201110096107 A CN201110096107 A CN 201110096107A CN 102226740 B CN102226740 B CN 102226740B
- 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.)
- Expired - Fee Related
Links
Images
Landscapes
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (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 using in bearing failure diagnosis.
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 huge economic loss.Therefore, diagnose out the fault features of bearing to avoiding the generation of catastrophic failure, guarantee that 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 processed, accurately extract fault characteristic signals from contain noisy signal, be one of study hotspot of current fault diagnosis.The method that adopts is utilized the difference on signal and 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 very noisy.Secondly, the bearing fault signal is comprised of fault characteristic signals and ground unrest.A large amount of ground unrests 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 affected the normal operation of rotary machine.
Summary of the invention
The object of the invention is to for the deficiencies in the prior art, a kind of Bearing Fault Detection Method based on adding periodic signal control accidental resonance is provided.
The objective of the invention is to be achieved through the following technical solutions: a kind of Bearing Fault Detection Method based on adding 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) will become bearing fault signal function after yardstick to bistable system, analyze the power spectrum of bistable system output, recover 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 as control signal, regulate the amplitude of control signal, thereby produce artificially or strengthen accidental resonance, detect the bearing fault characteristics signal.
Further, described step (1) is specially: degree of will speed up sensor is fixed on shaking table, and the vibration acceleration signal that utilizes acquisition system to gather bearing 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
, (the fault-signal characteristic frequency is to make 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: will become bearing fault signal function after yardstick to bistable system, and 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
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
High and the Kramers escape rate of bistable system potential barrier changes, thereby can produce artificially or strengthen accidental resonance, effectively strengthens the bistable system output power spectrum in the spectrum value at frequency input signal place, realize the control of accidental resonance, detect the bearing fault characteristics signal.
The invention has the beneficial effects as follows, the present invention is by regulating continuously the amplitude of control signal, high and the Kramers escape rate of bistable system potential barrier changes, thereby can produce artificially or strengthen accidental resonance, effectively strengthen the spectrum value of bistable system output power spectrum at the frequency input signal place, finally can detect exactly the bearing fault characteristics signal.The bearing fault signal can effectively amplify fault characteristic signals by accidental resonance, improve the signal to noise ratio (S/N ratio) of fault characteristic signals, obtain exactly the fault characteristic signals frequency, the method has realized effective control of accidental resonance, for the equipment failure early detection provides a kind of new method.The method also is applicable to other field and relates to Detection of Weak Signals in very noisy, and the application that can widen accidental resonance has a good application prospect.
Description of drawings
Fig. 1 adds the frequency detecting theory diagram that periodic signal is controlled accidental resonance.
Fig. 2 is the bearing vibration signal power spectrum chart.
Fig. 3 is the bearing vibration signal power spectrum chart of 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 and add the Bearing Fault Detection Method that periodic signal is controlled 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 shaking table, and the vibration acceleration signal that utilizes acquisition system to gather bearing 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
, (the fault-signal characteristic frequency is to make 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, will become bearing fault signal function after yardstick to bistable system, analyze the power spectrum of bistable system output, recover the collection yardstick of actual measurement bearing fault signal by the frequency compression scale ratio;
Bearing fault signal function after the change yardstick to bistable system, by analyzing the power spectrum of bistable system output, is caught 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 as control signal, regulate the amplitude of control signal, thereby produce artificially or strengthen accidental resonance, detect the bearing fault characteristics signal;
Add the single-frequency periodic signal
Act on bistable system as control signal, by regulating the amplitude of control signal
High and the Kramers escape rate of bistable system potential barrier changes, thereby can produce artificially or strengthen accidental resonance, effectively strengthens the bistable system output power spectrum in the spectrum value at frequency input signal place, realize the control of accidental resonance, detect the bearing fault characteristics signal.
By the following examples content of the present invention is done further explanation.With the method, the bearing fault signal is processed.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.Because the generation that often has much noise and accidental resonance in the actual field environment also needs suitable noise, therefore, with noise intensity
White Gaussian noise as background noise be increased to and obtain mixed signal in bearing vibration signal, power spectrum chart is as shown in Figure 3.As can be seen from Figure 3, there is no obvious fault characteristic information.The initialization system structural parameters
,
And do not exist and add periodic signal namely
, the frequency compression scale ratio
, the compression sampling frequency is
The compressed scale ratio of mixed signal
Be applied to bistable system after linear compression, the bistable system output power spectrum as shown in Figure 4.When adopting mode additional parameter shown in Figure 1 to be
,
Single-frequency signals act on bistable system as control signal, 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 an obvious spectrum peak at the place, and is also original through dimensions in frequency
, this frequency is the frequency of fault characteristic signals, and its frequency theory value is
Claims (5)
1. one kind based on the Bearing Fault Detection Method that adds periodic signal and 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) will become bearing fault signal function after yardstick to bistable system, analyze the power spectrum of bistable system output, recover 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 as control signal, regulate the amplitude of control signal, thereby produce artificially or strengthen accidental resonance, detect the bearing fault characteristics signal.
2. the Bearing Fault Detection Method based on adding periodic signal and control accidental resonance according to claim 1, it is characterized in that, described step (1) is specially: degree of will speed up sensor is fixed on shaking table, and the vibration acceleration signal that utilizes acquisition system to gather bearing is the bearing fault signal.
3. the Bearing Fault Detection Method based on adding periodic signal control accidental resonance according to claim 1, is characterized in that, described step (2) is specially: according to frequency compression scale ratio R definition compression sampling frequency f
sr=f
s/ R, f
sActual samples frequency for fault-signal; Obtaining the numerical evaluation step-length by the compression sampling frequency is Δ t=1/f
sr, make each frequency content of bearing fault signal by frequency compression scale ratio R linear compression, thus the characteristic frequency boil down to f of bearing fault signal
r=f
0/ R, f
0Be the fault-signal characteristic frequency, make it to satisfy the condition of the theoretical medium and small frequency signal of the existing adiabatic approximation of accidental resonance.
4. a kind of Bearing Fault Detection Method based on adding periodic signal and control accidental resonance according to claim 1, it is characterized in that, described step (3) is specially: will become bearing fault signal function after yardstick to bistable system, by analyzing the power spectrum of bistable system output, catch the characteristic frequency of fault-signal, recovering the fault-signal characteristic frequency by frequency compression scale ratio R at last is f
0=f
rR.
5. a kind of Bearing Fault Detection Method based on adding periodic signal and control accidental resonance according to claim 1, it is characterized in that, described step (4) is specially: add single-frequency periodic signal Bcos (Ω t) and act on bistable system as control signal, by regulating the amplitude B of control signal, high and the Kramers escape rate of bistable system potential barrier changes, thereby can produce artificially or strengthen accidental resonance, effectively strengthen the bistable system output power spectrum in the spectrum value at frequency input signal place, realize the control of accidental resonance, detect the bearing fault characteristics 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 CN102226740A (en) | 2011-10-26 |
| CN102226740B true 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 (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9186793B1 (en) | 2012-08-31 | 2015-11-17 | Brain Corporation | Apparatus and methods for controlling attention of a robot |
| US9242372B2 (en) | 2013-05-31 | 2016-01-26 | Brain Corporation | Adaptive robotic interface apparatus and methods |
| US9248569B2 (en) | 2013-11-22 | 2016-02-02 | Brain Corporation | Discrepancy detection apparatus and methods for machine learning |
| US9296101B2 (en) | 2013-09-27 | 2016-03-29 | Brain Corporation | Robotic control arbitration apparatus and methods |
| US9314924B1 (en) | 2013-06-14 | 2016-04-19 | Brain Corporation | Predictive robotic controller apparatus and methods |
| US9358685B2 (en) | 2014-02-03 | 2016-06-07 | Brain Corporation | Apparatus and methods for control of robot actions based on corrective user inputs |
| US9373038B2 (en) | 2013-02-08 | 2016-06-21 | Brain Corporation | Apparatus and methods for temporal proximity detection |
| US9384443B2 (en) | 2013-06-14 | 2016-07-05 | Brain Corporation | Robotic training apparatus and methods |
| US9412041B1 (en) | 2012-06-29 | 2016-08-09 | Brain Corporation | Retinal apparatus and methods |
| US9463571B2 (en) | 2013-11-01 | 2016-10-11 | Brian Corporation | Apparatus and methods for online training of robots |
| US9579789B2 (en) | 2013-09-27 | 2017-02-28 | Brain Corporation | Apparatus and methods for training of robotic control arbitration |
| US9597797B2 (en) | 2013-11-01 | 2017-03-21 | Brain Corporation | Apparatus and methods for haptic training of robots |
Families Citing this family (80)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9122994B2 (en) | 2010-03-26 | 2015-09-01 | Brain Corporation | Apparatus and methods for temporally proximate object recognition |
| US9311593B2 (en) | 2010-03-26 | 2016-04-12 | Brain Corporation | Apparatus and methods for polychronous encoding and multiplexing in neuronal prosthetic devices |
| US9405975B2 (en) | 2010-03-26 | 2016-08-02 | Brain Corporation | Apparatus and methods for pulse-code invariant object recognition |
| US8315305B2 (en) | 2010-03-26 | 2012-11-20 | Brain Corporation | Systems and methods for invariant pulse latency coding |
| US9906838B2 (en) | 2010-07-12 | 2018-02-27 | Time Warner Cable Enterprises Llc | Apparatus and methods for content delivery and message exchange 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 |
| US8990133B1 (en) | 2012-12-20 | 2015-03-24 | Brain Corporation | Apparatus and methods for state-dependent learning in spiking neuron networks |
| US9070039B2 (en) | 2013-02-01 | 2015-06-30 | Brian Corporation | Temporal winner takes all spiking neuron network sensory processing apparatus and methods |
| US9566710B2 (en) | 2011-06-02 | 2017-02-14 | Brain Corporation | Apparatus and methods for operating robotic devices using selective state space training |
| US9047568B1 (en) | 2012-09-20 | 2015-06-02 | Brain Corporation | Apparatus and methods for encoding of sensory data using artificial spiking neurons |
| US9147156B2 (en) | 2011-09-21 | 2015-09-29 | Qualcomm Technologies Inc. | Apparatus and methods for synaptic update in a pulse-coded network |
| US8725662B2 (en) | 2011-09-21 | 2014-05-13 | Brain Corporation | Apparatus and method for partial evaluation of synaptic updates based on system events |
| US8719199B2 (en) | 2011-09-21 | 2014-05-06 | Brain Corporation | Systems and methods for providing a neural network having an elementary network description for efficient implementation of event-triggered plasticity rules |
| US8725658B2 (en) | 2011-09-21 | 2014-05-13 | Brain Corporation | Elementary network description for efficient memory management in neuromorphic systems |
| US9460387B2 (en) | 2011-09-21 | 2016-10-04 | Qualcomm Technologies Inc. | Apparatus and methods for implementing event-based updates in neuron networks |
| 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 |
| 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 |
| US9104186B2 (en) | 2012-06-04 | 2015-08-11 | 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 |
| US9015092B2 (en) | 2012-06-04 | 2015-04-21 | Brain Corporation | Dynamically reconfigurable stochastic learning apparatus and methods |
| 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 |
| US9117176B2 (en) | 2011-09-21 | 2015-08-25 | Qualcomm Technologies Inc. | Round-trip engineering apparatus and methods for neural networks |
| US9146546B2 (en) | 2012-06-04 | 2015-09-29 | Brain Corporation | Systems and apparatus for implementing task-specific learning using spiking neurons |
| US10210452B2 (en) | 2011-09-21 | 2019-02-19 | Qualcomm Incorporated | High level neuromorphic network description apparatus and methods |
| 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 |
| 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 |
| US9440352B2 (en) | 2012-08-31 | 2016-09-13 | Qualcomm Technologies Inc. | Apparatus and methods for robotic learning |
| US9189730B1 (en) | 2012-09-20 | 2015-11-17 | Brain Corporation | Modulated stochasticity spiking neuron network controller apparatus and methods |
| US9367798B2 (en) | 2012-09-20 | 2016-06-14 | Brain Corporation | Spiking neuron network adaptive control apparatus and methods |
| US8793205B1 (en) | 2012-09-20 | 2014-07-29 | Brain Corporation | Robotic learning and evolution apparatus |
| US9311594B1 (en) | 2012-09-20 | 2016-04-12 | Brain Corporation | Spiking neuron network apparatus and methods for encoding of sensory data |
| 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 |
| US9111226B2 (en) | 2012-10-25 | 2015-08-18 | Brain Corporation | Modulated plasticity apparatus and methods for spiking neuron network |
| US9183493B2 (en) | 2012-10-25 | 2015-11-10 | Brain Corporation | Adaptive plasticity apparatus and methods for spiking neuron network |
| US9218563B2 (en) | 2012-10-25 | 2015-12-22 | Brain Corporation | Spiking neuron sensory processing apparatus and methods for saliency detection |
| 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 |
| US9195934B1 (en) | 2013-01-31 | 2015-11-24 | Brain Corporation | Spiking neuron classifier apparatus and methods using conditionally independent subsets |
| US8996177B2 (en) | 2013-03-15 | 2015-03-31 | Brain Corporation | Robotic training apparatus and methods |
| US9764468B2 (en) | 2013-03-15 | 2017-09-19 | Brain Corporation | Adaptive predictor apparatus and methods |
| US9008840B1 (en) | 2013-04-19 | 2015-04-14 | Brain Corporation | Apparatus and methods for reinforcement-guided supervised learning |
| US9792546B2 (en) | 2013-06-14 | 2017-10-17 | Brain Corporation | Hierarchical robotic controller apparatus and methods |
| US9239985B2 (en) | 2013-06-19 | 2016-01-19 | Brain Corporation | Apparatus and methods for processing inputs in an artificial neuron network |
| US9436909B2 (en) | 2013-06-19 | 2016-09-06 | Brain Corporation | Increased dynamic range artificial neuron network apparatus and methods |
| 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 |
| US9364950B2 (en) | 2014-03-13 | 2016-06-14 | Brain Corporation | Trainable modular robotic methods |
| US9987743B2 (en) | 2014-03-13 | 2018-06-05 | Brain Corporation | Trainable modular robotic apparatus and methods |
| US9533413B2 (en) | 2014-03-13 | 2017-01-03 | Brain Corporation | Trainable modular robotic apparatus and methods |
| 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 |
| US9713982B2 (en) | 2014-05-22 | 2017-07-25 | Brain Corporation | Apparatus and methods for robotic operation using video imagery |
| US9939253B2 (en) | 2014-05-22 | 2018-04-10 | Brain Corporation | Apparatus and methods for distance estimation using multiple image sensors |
| 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 |
| US9849588B2 (en) | 2014-09-17 | 2017-12-26 | Brain Corporation | Apparatus and methods for remotely controlling robotic devices |
| US9821470B2 (en) | 2014-09-17 | 2017-11-21 | Brain Corporation | Apparatus and methods for context determination using real time sensor data |
| US9860077B2 (en) | 2014-09-17 | 2018-01-02 | Brain Corporation | Home animation apparatus and methods |
| US9579790B2 (en) | 2014-09-17 | 2017-02-28 | Brain Corporation | Apparatus and methods for removal of learned behaviors in robots |
| US10055850B2 (en) | 2014-09-19 | 2018-08-21 | Brain Corporation | Salient features tracking apparatus and methods using visual initialization |
| US9630318B2 (en) | 2014-10-02 | 2017-04-25 | Brain Corporation | Feature detection apparatus and methods for training of robotic navigation |
| 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 |
| 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 |
| US9717387B1 (en) | 2015-02-26 | 2017-08-01 | 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 |
| 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 |
| 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 |
| 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 |
| CN110763465B (en) * | 2019-10-22 | 2021-04-27 | 中国计量大学 | Bearing early fault signal detection system based on tristable characteristic with damping |
| 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 |
Family Cites Families (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
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9412041B1 (en) | 2012-06-29 | 2016-08-09 | Brain Corporation | Retinal apparatus and methods |
| US9186793B1 (en) | 2012-08-31 | 2015-11-17 | Brain Corporation | Apparatus and methods for controlling attention of a robot |
| US9373038B2 (en) | 2013-02-08 | 2016-06-21 | Brain Corporation | Apparatus and methods for temporal proximity detection |
| US9242372B2 (en) | 2013-05-31 | 2016-01-26 | Brain Corporation | Adaptive robotic interface apparatus and methods |
| US9314924B1 (en) | 2013-06-14 | 2016-04-19 | Brain Corporation | Predictive robotic controller apparatus and methods |
| US9384443B2 (en) | 2013-06-14 | 2016-07-05 | Brain Corporation | Robotic training apparatus and methods |
| US9296101B2 (en) | 2013-09-27 | 2016-03-29 | Brain Corporation | Robotic control arbitration apparatus and methods |
| US9579789B2 (en) | 2013-09-27 | 2017-02-28 | Brain Corporation | Apparatus and methods for training of robotic control arbitration |
| US9463571B2 (en) | 2013-11-01 | 2016-10-11 | Brian Corporation | Apparatus and methods for online training of robots |
| US9597797B2 (en) | 2013-11-01 | 2017-03-21 | Brain Corporation | Apparatus and methods for haptic training of robots |
| US9248569B2 (en) | 2013-11-22 | 2016-02-02 | Brain Corporation | Discrepancy detection apparatus and methods for machine learning |
| US9358685B2 (en) | 2014-02-03 | 2016-06-07 | Brain Corporation | Apparatus and methods for control of robot actions based on corrective user inputs |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102226740A (en) | 2011-10-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102226740B (en) | Bearing fault detection method based on manner of controlling stochastic resonance by external periodic signal | |
| CN109883703B (en) | Fan bearing health monitoring and diagnosing method based on vibration signal coherent cepstrum analysis | |
| CN102155984B (en) | General vibration signal measuring system of fan | |
| CN103335844A (en) | Fault detection method for adaptive stochastic resonance bearing | |
| RU2015144130A (en) | DYNAMIC CONTROL OF MOTION SPEED SELECTION TO CHANGE ENERGY CONSUMPTION | |
| CN102645270A (en) | Intelligent dual-mode vibration sensor for rotary machinery | |
| CN107101827B (en) | A kind of low-speed heavy-loaded gear crack fault online test method | |
| CN105628381A (en) | Reciprocating compressor bearing fault diagnosis method based on improved local mean value decomposition | |
| CN103245913B (en) | The method and system of Generator Set sub-synchronous oscillation input and analysis | |
| CN103076168A (en) | Diagnosis method for mechanical faults of circuit breaker | |
| CN105021706A (en) | Grinding wheel broken state early warning recognition device and method | |
| CN103712792A (en) | Fault diagnosis method for wind-power gear case | |
| CN101251445A (en) | Method for analysis of fractal characteristic of rotating machinery bump-scrape acoustic emission signal | |
| CN109632291A (en) | A kind of Fault Diagnosis of Gear Case method based on polynary mode decomposition-transfer entropy | |
| CN112487882B (en) | Method for generating non-sparse index-guided enhanced envelope spectrum based on spectrum coherence | |
| CN105303181A (en) | Stochastic resonance weak impact feature enhancement extraction method on the basis of sliding window | |
| CN104359685A (en) | Diesel engine fault identification method | |
| CN116067657A (en) | Rolling bearing fault diagnosis method and system | |
| Zheng et al. | Zero-phase filter-based adaptive Fourier decomposition and its application to fault diagnosis of rolling bearing | |
| Shi et al. | Sound-aided fault feature extraction method for rolling bearings based on stochastic resonance and time-domain index fusion | |
| Ma et al. | Compound fault diagnosis of wind turbine bearing under ultra-low speed operations using generalized sparse spectral coherence | |
| CN109558041A (en) | Tip clearance signal acquisition, processing and the transmission method accelerated based on GPU | |
| CN111507305B (en) | Fractional order self-adaptive stochastic resonance bearing fault diagnosis method based on WCSNR | |
| CN102680080B (en) | Unsteady-state signal detection method based on improved self-adaptive morphological filtering | |
| Wang et al. | Advances in vibration analysis and modeling of large rotating mechanical equipment in mining arena: A review |
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 |