机械工程学报
機械工程學報
궤계공정학보
CHINESE JOURNAL OF MECHANICAL ENGINEERING
2014年
19期
97-104
,共8页
张洁%高宏力%陈春俊%傅攀
張潔%高宏力%陳春俊%傅攀
장길%고굉력%진춘준%부반
盲源分离%全局最优信噪比算法%非平稳信号%高速列车
盲源分離%全跼最優信譟比算法%非平穩信號%高速列車
맹원분리%전국최우신조비산법%비평은신호%고속열차
blind source separation%globally optimal snr algorithm%nonstationary signal%high speed train
高速列车具有若干时变激励源,传统的时频分析方法只能对观测的混合振动的总体强度分布、时频域结构加以分析,不能分离出与各振源对应的信号分量从而明晰振源状态与故障特征。盲源分离是一种可行的分析方法,但由于高速列车振动信号具有时变振源数目、时变信号长度、受车速调制的变频非平稳等特征,传统的盲源分离方法不适用。为了提高高速列车非平稳信号的盲源分离效果,基于自适应滤波理论提出全局最优信噪比盲源分离新方法,并对其可分离性的判别依据进行论证。新方法的有效性经仿真计算和实测数据分析得到验证。研究表明:新方法对高速列车时变非平稳信号的盲源分离效果优于传统的基于非线性函数的盲源分离方法和基于高阶累积量的盲源分离方法。
高速列車具有若榦時變激勵源,傳統的時頻分析方法隻能對觀測的混閤振動的總體彊度分佈、時頻域結構加以分析,不能分離齣與各振源對應的信號分量從而明晰振源狀態與故障特徵。盲源分離是一種可行的分析方法,但由于高速列車振動信號具有時變振源數目、時變信號長度、受車速調製的變頻非平穩等特徵,傳統的盲源分離方法不適用。為瞭提高高速列車非平穩信號的盲源分離效果,基于自適應濾波理論提齣全跼最優信譟比盲源分離新方法,併對其可分離性的判彆依據進行論證。新方法的有效性經倣真計算和實測數據分析得到驗證。研究錶明:新方法對高速列車時變非平穩信號的盲源分離效果優于傳統的基于非線性函數的盲源分離方法和基于高階纍積量的盲源分離方法。
고속열차구유약간시변격려원,전통적시빈분석방법지능대관측적혼합진동적총체강도분포、시빈역결구가이분석,불능분리출여각진원대응적신호분량종이명석진원상태여고장특정。맹원분리시일충가행적분석방법,단유우고속열차진동신호구유시변진원수목、시변신호장도、수차속조제적변빈비평은등특정,전통적맹원분리방법불괄용。위료제고고속열차비평은신호적맹원분리효과,기우자괄응려파이론제출전국최우신조비맹원분리신방법,병대기가분리성적판별의거진행론증。신방법적유효성경방진계산화실측수거분석득도험증。연구표명:신방법대고속열차시변비평은신호적맹원분리효과우우전통적기우비선성함수적맹원분리방법화기우고계루적량적맹원분리방법。
There are a lot of time-varying drive sources in high speed train. The traditional time-frequency analysis methods could analysis the magnitude and the spectrum characteristics of the compound vibration, but couldn’t separate the source signals to know their properties and failure distribution. The vibration signal of the high speed train is nonstationary random signal modulated by velocity, and the number of sources as well as the length of signals are time-varying, the traditional blind source separation methods couldn’t deal with the difficult problem. A new blind source separation algorithm called globally optimal signal-to-noise ratio algorithm based on the adaptive filtering theory is proposed. The separability of the proposed method is deduced. The simulation and test analysis results show that the proposed method is effective, and obtains more satisfactory separation quality than the classical blind source separation methods based on nonlinearity function and high-order cumulant in nonstationary signal analysis of high speed train.