Decoupling the aliased spectra of rolling bearing with multispeed multiple frequency correlation

A multispeed multiple frequency correlation method for decoupling aliased spectra when defect frequencies are approximate to integral multiple of the shaft speed is proposed for fault diagnosis of rolling bearing. Firstly, multiple frequencies at various shaft rotation speeds are obtained using the fast Fourier transform (FFT). Secondly, the correlation coefficients between the amplitude growth trend of the multiple frequencies and the expected trend are calculated. Thirdly, correlation coefficients of assumed inner and outer race fault multiple frequencies are acquired by averaging correlation coefficients of possible aliased multiple frequencies for each bearing failure state. Finally, the correlation coefficient curves of different assumed fault multiple frequencies are compared, enabling operational assessment of the rolling bearing. The experimental results show that the multispeed multiple frequency correlation method is an effective tool for decoupling aliased spectra and diagnosing bearing faults

When Mining Electric Locomotives Meet Reinforcement Learning

As the most important auxiliary transportation equipment in coal mines, mining electric locomotives are mostly operated manually at present. However, due to the complex and ever-changing coal mine environment, electric locomotive safety accidents occur frequently these years. A mining electric locomotive control method that can adapt to different complex mining environments is needed. Reinforcement Learning (RL) is concerned with how artificial agents ought to take actions in an environment so as to maximize reward, which can help achieve automatic control of mining electric locomotive. In this paper, we present how to apply RL to the autonomous control of mining electric locomotives. To achieve more precise control, we further propose an improved epsilon-greedy (IEG) algorithm which can better balance the exploration and exploitation. To verify the effectiveness of this method, a co-simulation platform for autonomous control of mining electric locomotives is built which can complete closed-loop simulation of the vehicles. The simulation results show that this method ensures the locomotives following the front vehicle safely and responding promptly in the event of sudden obstacles on the road when the vehicle in complex and uncertain coal mine environments

The influence of SS-quasinormality of some subgroups on the structure of finite groups

AbstractThe following concept is introduced: a subgroup H of the group G is said to be SS-quasinormal (Supplement-Sylow-quasinormal) in G if H possesses a supplement B such that H permutes with every Sylow subgroup of B. Groups with certain SS-quasinormal subgroups of prime power order are studied. For example, fix a prime divisor p of |G| and a Sylow p-subgroup P of G, let d be the smallest generator number of P and Md(P) denote a family of maximal subgroups P1,…,Pd of P satisfying ⋂i=1d(Pi)=Φ(P), the Frattini subgroup of P. Assume that the group G is p-solvable and every member of some fixed Md(P) is SS-quasinormal in G, then G is p-supersolvable

Experimental investigation of feedforward inverse control with disturbance observer for acceleration tracking of electro-hydraulic shake table

Electro-hydraulic shake tables (EHSTs) are indispensable equipments in laboratory for evaluating structural performance subject to vibration environment. A novel feedforward inverse control with disturbance observer strategy is proposed in this paper in order to improve the acceleration tracking performance of the EHST system. The EHST system is firstly controlled by the three variable controller (TVC) to obtain a coarse time waveform replication accuracy, and then the parametric transfer function of the TVC controlled EHST system is identified with the H1 estimation method and complex curving fitting technology. Next, the zero magnitude error tracking control technology is employed to deal with the estimated non-minimum phase transfer function so as to design a stable and casual inverse model, and the proposed controller comprised of feedforward inverse controller and disturbance observer is further established based on the designed inverse model. Therefore, the proposed algorithm combines the virtues of feedforward inverse control and disturbance observer. The proposed algorithm is firstly programmed by MATLAB/Simulink software and then is compiled to an Advantech computer with real-time operating system for implementation. Finally, experiments are carried out on a unidirectional EHST system and the results demonstrate that a better acceleration tracking performance is achieved with the proposed controller than with the other conventional controllers

Fault diagnosis of rotating machinery based on time-frequency decomposition and envelope spectrum analysis

In order to raise the working reliability of rotating machinery in real applications and reduce the loss caused by unintended breakdowns, a new method based on improved ensemble empirical mode decomposition (EEMD) and envelope spectrum analysis is proposed for fault diagnosis in this paper. First, the collected vibration signals are decomposed into a series of intrinsic mode functions (IMFs) by the improved EEMD (IEEMD). Then, the envelope spectrums of the selected decompositions of IEEMD are analyzed to calculate the energy values within the frequency bands around speed and bearing fault characteristic frequencies (CDFs) as features for fault diagnosis based on support vector machine (SVM). Experiments are carried out to test the effectiveness of the proposed method. Experimental results show that the proposed method can effectively extract fault characteristics and accurately realize classification of bearing under normal, inner race fault, ball fault and outer race fault