齿诊的脏腑部位分属

根据《内经》及张颖清的《生物全息律》推断，各部位牙齿有一定的脏腑部位分属。通过对齿痛的治疗用药归经分析，及参考舌诊、脉诊的脏腑部位分属规律，从而确定了齿诊的脏腑部位分属，即上切牙属心，下切牙属肾，上尖牙及前磨牙属胃，下尖牙及前磨牙属脾，上左磨牙属胆，下左磨牙属肝，上右磨牙属大肠，下右磨牙属肺。

北京市食药监管局开发区分局加强对义齿诊所监管力度

北京市食品药品监督管理局开发区分局对辖区内牙科诊所开展了专项监督检查。根据《北京市食品药品监督管理局北京市卫生和计划生育委员会关于医疗器械使用监管工作有关事宜的通告》的文件要求，该食药监管分局制定了此次专项监督检查的记录表，重点针对采购验收、运输储存、使用维护等三个方面对辖区牙科诊所进行了现场检查。

几种中医诊法的研究进展

诊法是中医诊断学中的重要内容,建国以来,由于各地医者的努力,舌诊、腹诊、目诊、耳诊、穴位诊等各种中医诊法的研究取得了可喜的成绩。本文拟就颜面色诊、山根诊、唇诊、齿诊、颊粘膜诊、唾液诊的研究概况作一综述,以供同道参考。 一、颜色色诊 颜面色诊主要是观察病人两侧颧部和颊部的色泽。有人观察到面部蟹爪纹的分布主病有一定规律性,心病以颞区布纹为主,肝病、肝肾同病以鼻、颊区为主,肺病以颧区为主,

Fault Detection and Diagnosis of a Gearbox in Marine Propulsion Systems Using Bispectrum Analysis and Artificial Neural Networks

A marine propulsion system is a very complicated system composed of many mechanical components.As a result,the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other components including a diesel engine and main shaft.It is therefore imperative to assess the coupling effect on diagnostic reliability in the process of gear fault diagnosis.For this reason,a fault detection and diagnosis method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for the gearbox with consideration given to the impact of the other components in marine propulsion systems.To monitor the gear conditions,the bispectrum analysis was first employed to detect gear faults.The amplitude-frequency plots containing gear characteristic signals were then attained based on the bispectrum technique,which could be regarded as an index actualizing forepart gear faults diagnosis.Both the back propagation neural network (BPNN) and the radial-basis function neural network (RBFNN) were applied to identify the states of the gearbox.The numeric and experimental test results show the bispectral patterns of varying gear fault severities are different so that distinct fault features of the vibrant signal of a marine gearbox can be extracted effectively using the bispectrum,and the ANN classification method has achieved high detection accuracy.Hence,the proposed diagnostic techniques have the capability of diagnosing marine gear faults in the earlier phases,and thus have application importance.

DETECTION OF INCIPIENT DEFECTS IN A GEARBOX BY DEMODULATION OF MESHING VIBRATION SIGNATURE

This paper presents an investigation into the detection of incipient fatigue cracks in a gearbox.On the basis of modulation theory,a signal processing technique is proposed in the paper to isolate irregular signals from complex vibration signals,and to obtain the amplitude and phase modulation functions without use of any tachometers or frequency multiplier.It has been shown that this signal processing method is useful for enhancement of the signal containing the fault features and effective in the detection of incipient defects in the gearbox.

Impulsive component extraction using shift-invariant dictionary learning and its application to gear-box bearing early fault diagnosis

The impulsive components induced by bearing faults are key features for assessing gear-box bearing faults.However,because of heavy background noise and the interferences of other vibrations,it is difficult to extract these impulsive components caused by faults,particularly early faults,from the measured vibration signals.To capture the high-level structure of impulsive components embedded in measured vibration signals,a dictionary learning method called shift-invariant K-means singular value decomposition(SI-K-SVD)dictionary learning is used to detect the early faults of gear-box bearings.Although SI-K-SVD is more flexible and adaptable than existing methods,the improper selection of two SI-K-SVD-related parameters,namely,the number of iterations and the pattern lengths,has an adverse influence on fault detection performance.Therefore,the sparsity of the envelope spectrum(SES)and the kurtosis of the envelope spectrum(KES)are used to select these two key parameters,respectively.SI-K-SVD with the two selected optimal parameter values,referred to as optimal parameter SI-K-SVD(OP-SI-K-SVD),is proposed to detect gear-box bearing faults.The proposed method is verified by both simulations and an experiment.Compared to the state-of-the-art methods,namely,empirical model decomposition,wavelet transform and K-SVD,OP-SI-K-SVD has better performance in diagnosing the early faults of a gear-box bearing.