Research on fault diagnosis method of rolling bearing based on AMD and LabVIEW

Aiming at the problem of rolling bearing fault diagnosis, a fault diagnosis method of rolling bearing is proposed based on analytical mode decomposition (AMD) and LabVIEW. For the fault feature frequency of rolling bearing is predictable, the AMD method can be used to extract the signal in the frequency band of fault characteristic frequency in rolling bearing signal, and seek frequency spectrum of vibration signal. If the spectrum contains fault characteristic frequency, then the rolling bearing fault can be diagnosed by vibration signal. A rolling bearing fault diagnosis system is developed based on LabVIEW and AMD, and the application of AMD algorithm is realized. The validity of the method is proved by the analysis of actual fault signal of rolling bearing

Phase Fluctuation Analysis in Functional Brain Networks of Scaling EEG for Driver Fatigue Detection

The characterization of complex patterns arising from electroencephalogram (EEG) is an important problem with significant applications in identifying different mental states. Based on the operational EEG of drivers, a method is proposed to characterize and distinguish different EEG patterns. The EEG measurements from seven professional taxi drivers were collected under different states. The phase characterization method was used to calculate the instantaneous phase from the EEG measurements. Then, the optimization of drivers’ EEG was realized through performing common spatial pattern analysis. The structures and scaling components of the brain networks from optimized EEG measurements are sensitive to the EEG patterns. The effectiveness of the method is demonstrated, and its applicability is articulated.</p

Nonlinear dynamic model and vibration response of faulty outer and inner race rolling element bearings

By considering the deep groove ball bearing as the research object, and using Hertzian contact theory and elastic mechanics and geometry, a nonlinear dynamic model is established herein for the study of vibration response of deep groove ball bearings having single defect on surfaces of inner and outer races. The outer race and inner race defect size parameters are introduced into this nonlinear dynamic model, and dynamic models of localized fault on outer race, inner race of rolling element bearing are simulated and analyzed by using Runge-Kutta method. Both simulated and experimental localized fault signals (acceleration signals) are subjected to the same diagnostic techniques; namely time domain waveform comparisons and envelope analysis. Then, the impact characteristic that reflects the fault severity in rolling element bearings is obtained from the time interval between two impact points. The simulating results are in accordance with experimental results, which proves the accuracy and practicality of the models used in engineering application. The characteristic defect frequencies and related harmonics are broadly investigated and presented herein. This proposed model provides theoretical basis for monitoring and fault diagnosis of rolling bears

Nonlinear Torsional Vibration Dynamics Behaviors of Rolling Mill’s Multi-DOF Main Drive System under Parametric Excitation

Considering the influence caused by joint angle, nonlinear damping, and nonlinear rigidity, the nonlinear torsional vibration dynamical modeling of the multi-DOF rolling mill’s main drive system is established. To analyze the coupled equations by analytic method, the equations are decoupled by transforming them into principal coordinates. The amplitude-frequency characteristic equations are obtained by multiscale method. Furthermore, numerical example based on the 1780 rolling mill’s main drive system of some Steel Co. is given to illustrate the effects of the resonance on the response of the system. The relationship between amplitude and frequency varies according to the parameters changes of nonlinear stiffness, nonlinear friction damping, torque disturbance, and joint angle. During the rolling process, the limited joint angles range is obtained and the variation rules of the joint angle caused by the nonlinear damping, nonlinear stiffness, and the disturbance torque are gained. The results present that the rolling mill can work more stably with the joint angle at a range from 2° to 8° by controlling the value of parameters. The research results provide a theoretical basis and reference for analyzing torsional vibration of rolling mill’s transmission system caused by joint angle

A weak fault diagnosis method for rotating machinery based on compressed sensing and stochastic resonance

Vibration signals used for rotating machinery fault diagnosis often constitute large amount of data. It is a big challenge to extract faults feature information from these data. Recently, a new sampling framework called compressed sensing has been proposed, which enables the recovery from a small set of measured data if the signals are sparse or compressible. In reality, the sparseness of the signals is not very well due to noise, so it is difficult and unavailing to recover the whole signal. Thus, a new mechanical fault diagnosis method is proposed in this paper. First, the machine fault vibration signals are pretreated by stochastic resonance. By this way, the fault signal drowned by noise is amplified and the sparseness of the signals is enhanced, which make it possible to apply compressed sensing. Second, fault features are extracted directly from the compressed data without recovering completely, which reduces the dimensionality of the measurement data and the complexity of algorithm. Finally, the effectiveness of the proposed method is proved by the experiments

A novel intelligent fault diagnosis method of rotating machinery based on deep learning and PSO-SVM

A novel intelligent fault diagnosis method based on deep learning and particle swarm optimization support vectors machine (PSO-SVM) is proposed. The method uses deep learning neural network (DNN) to extract fault features automatically, and then uses support vector machine to classify diagnose faults based on extracted features. DNN consists of a stack of denoising autoencoders. Through pre-training and fine-tuning of DNN, features of input parameters can be extracted automatically. This paper uses particle swarm optimization algorithm to select the best parameters for SVM. The extracted features from multiple hidden layers of DNN are used as the input of PSO-SVM. Experimental data is derived from the data of rolling bearing test platform of West University. The results demonstrate that deep learning can automatically extract fault feature, which removes the need for manual feature selection, various signal processing technologies and diagnosis experience, and improves the efficiency of fault feature extraction. Under the condition of small sample size, combining the features of the multiple hidden layers as the input into the PSO-SVM can significantly increase the accuracy of fault diagnosis

Oral Abstracts 7: RA ClinicalO37. Long-Term Outcomes of Early RA Patients Initiated with Adalimumab Plus Methotrexate Compared with Methotrexate Alone Following a Targeted Treatment Approach

Background: This analysis assessed, on a group level, whether there is a long-term advantage for early RA patients treated with adalimumab (ADA) + MTX vs those initially treated with placebo (PBO) + MTX who either responded to therapy or added ADA following inadequate response (IR). Methods: OPTIMA was a 78- week, randomized, controlled trial of ADA + MTX vs PBO + MTX in MTX-naïve early (<1 year) RA patients. Therapy was adjusted at week 26: ADA + MTX-responders (R) who achieved DAS28 (CRP) <3.2 at weeks 22 and 26 (Period 1, P1) were re-randomized to withdraw or continue ADA and PBO + MTX-R continued randomized therapy for 52 weeks (P2); IR-patients received open-label (OL) ADA + MTX during P2. This post hoc analysis evaluated the proportion of patients at week 78 with DAS28 (CRP) <3.2, HAQ-DI <0.5, and/or ΔmTSS ≤0.5 by initial treatment. To account for patients who withdrew ADA during P2, an equivalent proportion of R was imputed from ADA + MTX-R patients. Results: At week 26, significantly more patients had low disease activity, normal function, and/or no radiographic progression with ADA + MTX vs PBO + MTX (Table 1). Differences in clinical and functional outcomes disappeared following additional treatment, when PBO + MTX-IR (n = 348/460) switched to OL ADA + MTX. Addition of OL ADA slowed radiographic progression, but more patients who received ADA + MTX from baseline had no radiographic progression at week 78 than patients who received initial PBO + MTX. Conclusions: Early RA patients treated with PBO + MTX achieved comparable long-term clinical and functional outcomes on a group level as those who began ADA + MTX, but only when therapy was optimized by the addition of ADA in PBO + MTX-IR. Still, ADA + MTX therapy conferred a radiographic benefit although the difference did not appear to translate to an additional functional benefit. Disclosures: P.E., AbbVie, Merck, Pfizer, UCB, Roche, BMS—Provided Expert Advice, Undertaken Trials, AbbVie—AbbVie sponsored the study, contributed to its design, and participated in the collection, analysis, and interpretation of the data, and in the writing, reviewing, and approval of the final version. R.F., AbbVie, Pfizer, Merck, Roche, UCB, Celgene, Amgen, AstraZeneca, BMS, Janssen, Lilly, Novartis—Research Grants, Consultation Fees. S.F., AbbVie—Employee, Stocks. A.K., AbbVie, Amgen, AstraZeneca, BMS, Celgene, Centocor-Janssen, Pfizer, Roche, UCB—Research Grants, Consultation Fees. H.K., AbbVie—Employee, Stocks. S.R., AbbVie—Employee, Stocks. J.S., AbbVie, Amgen, AstraZeneca, BMS, Celgene, Centocor-Janssen, GlaxoSmithKline, Lilly, Pfizer (Wyeth), MSD (Schering-Plough), Novo-Nordisk, Roche, Sandoz, UCB—Research Grants, Consultation Fees. R.V., AbbVie, BMS, GlaxoSmithKline, Human Genome Sciences, Merck, Pfizer, Roche, UCB Pharma—Consultation Fees, Research Support. Table 1.Week 78 clinical, functional, and radiographic outcomes in patients who received continued ADA + MTX vs those who continued PBO + MTX or added open-label ADA following an inadequate response ADA + MTX, n/N (%)a PBO + MTX, n/N (%)b Outcome Week 26 Week 52 Week 78 Week 26 Week 52 Week 78 DAS28 (CRP) <3.2 246/466 (53) 304/465 (65) 303/465 (65) 139/460 (30)*** 284/460 (62) 300/460 (65) HAQ-DI <0.5 211/466 (45) 220/466 (47) 224/466 (48) 150/460 (33)*** 203/460 (44) 208/460 (45) ΔmTSS ≤0.5 402/462 (87) 379/445 (86) 382/443 (86) 330/459 (72)*** 318/440 (72)*** 318/440 (72)*** DAS28 (CRP) <3.2 + ΔmTSS ≤0.5 216/462 (47) 260/443 (59) 266/443 (60) 112/459 (24)*** 196/440 (45) 211/440 (48)*** DAS28 (CRP) <3.2 + HAQ-DI <0.5 + ΔmTSS ≤0.5 146/462 (32) 168/443 (38) 174/443 (39) 82/459 (18)*** 120/440 (27)*** 135/440 (31)** aIncludes patients from the ADA Continuation (n = 105) and OL ADA Carry On (n = 259) arms, as well as the proportional equivalent number of responders from the ADA Withdrawal arm (n = 102). bIncludes patients from the MTX Continuation (n = 112) and Rescue ADA (n = 348) arms. Last observation carried forward: DAS28 (CRP) and HAQ-DI; Multiple imputations: ΔmTSS. ***P < 0.001 and **iP < 0.01, respectively, for differences between initial treatments from chi-squar

A Novel Piecewise Tri-Stable Stochastic Resonance System Driven by Dichotomous Noise

Stochastic resonance (SR) has been widely studied as a means of signal processing since its conception. Since SR is different from other denoising methods in nature, it can be used for not only feature extraction but also signal enhancement. Additive white Gaussian noise (AWGN) is often used as a driving source in SR research due to its convenience in numerical simulation and uniform distribution, but as a special noise, it is of great significance to study the SR principle of dichotomous noise as a driving source for nonlinear dynamics. In this paper, the method of piecewise tri-stable SR (PTSR) driven by dichotomous noise is studied, and it is verified that signal enhancement can still be achieved in the PTSR system. At the same time, the influence of the parameters of the PTSR system, periodic signal, and dichotomous noise on the mean of signal-to-noise ratio gain (SNR-GM) is analyzed. Finally, dichotomous noise and AWGN are used as the driving sources of the PTSR system, and the signal enhancement ability and noise resistance ability of the two drivers are compared

Mechanical Fault Feature Extraction under Underdamped Conditions Based on Unsaturated Piecewise Tri-Stable Stochastic Resonance

In the case of the rapid development of large machinery, the research of mechanical fault signal feature extraction is of great significance, it can not only ensure the development of the economy but also ensure safety. Stochastic resonance (SR) is of widespread use in feature extraction of mechanical fault signals due to its excellent signal extraction capability. Compared with an overdamped state, SR in an underdamped state is equivalent to one more filtering of the signal, so the signal-to-noise ratio (SNR) of the output signal will be further improved. In this article, based on the piecewise tri-stable SR (PTSR) obtained from previous studies, the feature extraction of mechanical fault signals is carried out under underdamped conditions, and it is found that the SNR of the output signal is further improved. The simulation signals and experimental signals are used to verify that PTSR has better output performance under underdamped conditions