轴承故障诊断的最优化随机共振方法分析

Optimized stochastic resonance method for bearing fault diagnosis

现代机械设备正朝着大型、复杂和高速方向发展,导致其长期在强噪声环境下运行,使得通过振动分析检测微弱故障变得极为困难。因此,从强噪声背景中提取微弱故障信号成为机械故障诊断的关键问题。随机共振利用噪声能量来加强特征信号能量,特别适合于现代机械设备微弱故障诊断,然而,共振系统结构参数对其输出结果影响较大。针对这一实际情况,为了更好地对故障轴承进行精确诊断,以随机共振理论为依据,提出了基于人工蜂群算法的自适应随机共振新方法。以随机共振输出信噪比作为算法的目标函数,利用人工蜂群算法搜索全局最优解,实现双稳系统参数的自适应调节,获得信噪比最大时的系统参数,最终实现从强噪声环境中检测出微弱信号。数值仿真和轴承故障诊断试验表明:该方法得到的输出频率谱故障频率峰值比经典随机共振方法得到的峰值高20%,可用于强噪声环境下轴承故障识别和诊断。

Modern machinery and equipment are moving in a large, complex, and high-speed direction. Machinery and equipment typically runs in a strong noise background and it is difficult to detect incipient faults through vibration analysis. It has been an important problem for fault diagnosis to extract the weak fault signals from a strong noise environment. Stochastic resonance （SR） is a phenomenon where a signal that is normally too weak to be detected by a sensor can be boosted by adding white noise to the signal, which contains a wide spectrum of frequencies. Therefore, SR can converse noise energy to signal energy, and then it is commonly used to enhance the signal-to-noise ratio （SNR） of a system output using the unavoidable environmental noise and it is suitable to detect the weak faults of rotary components in modern machinery and equipment. However, the structural parameters of a stochastic resonance system have a great impact on its output, and each input signal will correspond to a set of optimal structural parameters. An artificial bee colony algorithm has been proposed to be a rapid developed optimization algorithm in recent years for its fast convergence speed, high accuracy, and good global search capability. To deal with the actual situation and make an accurate detection for rolling element bearings, a new adaptive stochastic resonance method was developed using an artificial bee colony algorithm and stochastic resonance theory. In order to obtain the maximum stochastic resonance output SNR, the structural parameters of the system has been adaptively optimized by an artificial bee colony algorithm using the SNR as the objective function. ABC is one of the population based algorithms, the position of a food source represents a possible solution to the optimization problem, and the nectar amount of a food source corresponds to the quality （fitness） of the associated solution. The number of the employed bees （in an ABC model, the colony consists of three groups of bees, i.e., employed bees, onlookers, and scouts） was equal to the number of solutions in the population. Based on the method, the input signal could correspond to a set of optimal structural parameters and the weak fault signals were finally detected from strong environment noises. The comparison study between the present ACB-based SR and traditional SR was performed by a numerical simulation signal of cosine function with Gaussian white noise. The result showed that the feature frequency peaks in ACB-based SR were 70 percent higher than those in traditional SR. Finally, experimental investigation of a rolling bearing with an inner race fault in a Machinery Fault Simulator - Magnum （MFS-MG） was performed. Due to the fact that the sampling frequency was 25.6kHz, the experimental data should been preprocessed by a scale transformation and the scale transformation compression ratio R equaled to 5120 and the compression sampling frequency was 5Hz. Finally, the fault detection results showed that the presented method was favored to detect and diagnose rolling bearing faults from a strong noise environment. The peak values in the output frequency spectrum of the present method were higher by about 20 percent more than those of the classical SR.