Experimental Study On Fault Analysis And Fault Diagnosis Of The Key Equipment In Metallurgical Enterprise

There are some key important equipment in modern metallurgy enterprise production, they are often large, complex, high value, high automation and continuous operation and low-speed heavy-load. The work environment was bad, the impact load was also serious. The early defects fault diagnosis of these equipment components is very important. Equipment fault can be reflected by vibration, the machine running state information is contained in the vibration signal, so it is reliable and effective to condition monitor and fault diagnose according to the vibration signal of the key equipment. A slewing bearing platform is used as the of experimental research object in the laboratory, collect the normal condition and two fault types(gear surface wear and partial failure) data of slewing bearing equipment, Through hardware and software processing analysis, the basis of the research. With key equipment failure analysis and fault diagnosis in metallurgical enterprises production is provided.The main work of this thesis:(1)Study on the theoretical and practical of vibration characteristics and fault principle for the fan and ladle turret, which represent the key important equipment in modern metallurgy enterprise production.(2)The methods of signal processing are discussed, Stochastic resonance, instantaneous power spectrum and the order analysis and wavelet analysis theory are introduced in this paper.(3)A slewing bearing is used in the experimental study, collect and extract normal and gear surface wear, partial failure fault signal, then using wavelet de-noising method to denoise the signal processing, use time domain parameters fusion, wavelet- the Hilbert envelope, and the fault frequency domain and time domain parameter analysis based on the wavelet packet decomposition for data analysis.