基于信号共振稀疏分解的自动换刀装置故障诊断
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摘要
针对刀库中刀柄拉钉可产生松动的故障,研究一种基于信号共振稀疏分解的自动换刀装置故障诊断方法。以链式刀库为例,采集刀柄拉钉在不同旋转角度时自动换刀装置在自动换刀过程中所产生的振动信号,利用双可调品质因子小波变换的共振稀疏分解将所采集的振动信号分解成包含故障信息的周期瞬态低共振分量和自身运动的振荡谐波高共振分量。在此基础上,通过比对分析不同条件下的周期瞬态低共振分量信号,得到链式刀库自动换刀装置振动冲击成分与拉钉旋转角度的关系,并据此诊断自动换刀装置的故障。诊断结果显示,刀柄拉钉松动旋转角度为360°时,振动较大,该自动换刀装置需要进行维修,以增加其可靠性。研究结果可用于自动换刀装置的故障诊断,对于促进自动换刀装置健康状态监测方法的发展具有重大意义。
Aiming at the looseness faults of tool holder rivets in the chain tool magazine, a fault diagnosis method for the automatic tool changer of the chain tool magazine based on resonance sparse signal decomposition is studied. The vibration signal generated by the automatic tool changer of the chain tool magazine during the automatically changing tools is collected at different rotation angles of the tool holder rivets. With dual tunable quality factor wavelet transform, the collected vibration signal is decomposed into periodic transient low resonance component containing fault information the oscillating harmonic high resonance component of the self-motion. On this basis, the relationship between the vibration shock component of the automatic tool changer of the chain tool magazine and the rotation angle of the rivet is obtained by comparing the periodic transient low resonance component with each other under different conditions, and the automatic tool changer can be diagnosed. The diagnosis results show that when the tool holder rivet loosening angle is 360°, the vibration is larger, and the automatic tool changer needs to be repaired to increase its reliability. The research results can be used for fault diagnosis of automatic tool changer and have great significance for promoting the development of health monitoring methods of automatic tool changer.
Aiming at the looseness faults of tool holder rivets in the chain tool magazine, a fault diagnosis method for the automatic tool changer of the chain tool magazine based on resonance sparse signal decomposition is studied. The vibration signal generated by the automatic tool changer of the chain tool magazine during the automatically changing tools is collected at different rotation angles of the tool holder rivets. With dual tunable quality factor wavelet transform, the collected vibration signal is decomposed into periodic transient low resonance component containing fault information the oscillating harmonic high resonance component of the self-motion. On this basis, the relationship between the vibration shock component of the automatic tool changer of the chain tool magazine and the rotation angle of the rivet is obtained by comparing the periodic transient low resonance component with each other under different conditions, and the automatic tool changer can be diagnosed. The diagnosis results show that when the tool holder rivet loosening angle is 360°, the vibration is larger, and the automatic tool changer needs to be repaired to increase its reliability. The research results can be used for fault diagnosis of automatic tool changer and have great significance for promoting the development of health monitoring methods of automatic tool changer.
引文
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[12] Afonso M V,Bioucas-Dias J M,Figueiredo M A.An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems[J].IEEE Transactions on Image Processing,2011,20(3):681-695.
[2] Chang C F,Chen J J.Vibration monitoring of motorized spindles using spectral analysis techniques[J].Mechatronics,2009,19(5):726-734.
[3] 周兴平.加工中心ATC拔刀力检测系统的研制与试验研究[D].长春:吉林大学,2016.
[4] Ru Q Y,Robert X G.Base wavelet selection for bearing vibration signal analysis[J].International Journal of Wavelets Multiresolution & Information Processing,2009,7(4):411-426.
[5] Lei Y,Lin J,He Z,et al.A review on empirical mode decomposition in fault diagnosis of rotating machinery[J].Mechanical Systems & Signal Processing,2013,35(1-2):108-126.
[6] Selesnick I W.Wavelet transform with tunable Q-factor[J].IEEE Transactions on Signal Processing,2011,59(8):3560-3575.
[7] Chen X,Cai G,Cao H,et al.Condition assessment for automatic tool changer based on sparsity-enabled signal decomposition method[J].Mechatronics,2015,31:50-59.
[8] Bayram I,Selesnick I W.Frequency-domain design of overcomplete rational-dilation wavelet transforms[J].IEEE Transactions on Signal Processing,2009,57(8):2957-2972.
[9] 陈保家,汪新波,严文超,等.采用品质因子优化和子带重构的共振稀疏分解滚动轴承故障诊断方法[J].西安交通大学学报,2018,52(4):70-76,89.
[10] Selesnick I W.Resonance-based signal decomposition:A new sparsity-enabled signal analysis method[J].Signal Processing,2012,91(12):2793-2809.
[11] Elad M,Starck J L,Querre P,et al.Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA)[J].Applied & Computational Harmonic Analysis,2005,19(3):340-358.
[12] Afonso M V,Bioucas-Dias J M,Figueiredo M A.An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems[J].IEEE Transactions on Image Processing,2011,20(3):681-695.
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