基于构造性神经网络的模拟电路故障诊断研究
发布时间：2019-05-19 19:15
【摘要】：模拟电路故障诊断研究已有数十年历史,受元件容差、非线性、温漂等因素影响,该课题一直是研究的难点和热点。电子器件中,模拟电路所占比例不大,但故障问题出现最多,电子器件运行可靠性很大程度上依赖于模拟电路的可靠性。传统方法在电路规模日趋增大的背景下表现出数据处理能力弱、诊断时间长、诊断过程复杂等局限性,人工智能方法特别是神经网络方法为其提供了新的研究方向,能很好适应非线性电路诊断,不依赖具体电路,降低诊断难度,但在诊断时间、诊断精度、纠错容错方面仍表现不足,并且建模过程复杂。基于覆盖理论构造性神经网络是近年来提出的新型神经网络方法,它相比于传统神经网络具有建模简单、鲁棒性好、运算能力强的优点,适用于海量数据、复杂环境等情况下的工业应用,特别能大大降低运算时间。本文以神经网络理论为基础,克服现有故障诊断系统需要提取故障特征,故障建模过程复杂,系统运行中难以实现知识扩充等问题,提出将构造性神经网络方法应用于模拟电路故障诊断中,取得良好的诊断结果。本文首先以M P神经元球面模型为基础,建立基于球面领域的构造性神经网络,对模拟电路具有±4%扰动故障样本进行诊断能达到100%诊断精度;然后针对具有±15%扰动样本某些故障无法诊断问题,通过设定拒识模式并通过增加神经元方法对无法诊断故障进行学习扩充,重新训练神经网络,能对新故障完全诊断并提升整体诊断精度;针对实际工业应用中需要处理海量数据,诊断系统存在优化约简的问题,本文采用领域覆盖和模糊覆盖算法对神经网络进行优化构造,诊断范围从最大软故障扩大为所有软故障模式,诊断精度分别能达到89.3%和94.9%,并且能降低神经元个数,减小计算难度、计算量,降低诊断时间,同时使用模糊覆盖算法对最大软故障模式进行诊断,单选诊断率为85.71%,三选能实现100%诊断。实验证明本文方法具有很强容错能力,泛化能力好,特别适合复杂环境下电路故障诊断,具有良好发展前景。
[Abstract]:The research on fault diagnosis of analog circuits has been studied for decades. Affected by element tolerance, nonlinear, temperature drift and other factors, this subject has always been a difficult and hot research topic. Among electronic devices, analog circuits account for a small proportion, but the fault problems occur the most. The reliability of electronic devices depends on the reliability of analog circuits to a large extent. Under the background of the increasing size of the circuit, the traditional method shows the limitations of weak data processing ability, long diagnosis time and complex diagnosis process. Artificial intelligence method, especially neural network method, provides a new research direction for it. It can adapt to nonlinear circuit diagnosis, does not rely on specific circuits, and reduces the difficulty of diagnosis, but it is still insufficient in diagnosis time, diagnosis accuracy, error correction and fault tolerance, and the modeling process is complex. The constructive neural network based on coverage theory is a new neural network method proposed in recent years. Compared with the traditional neural network, it has the advantages of simple modeling, good robustness and strong computing ability, and is suitable for massive data. The industrial application in complex environment and so on can greatly reduce the operation time. In this paper, based on the theory of neural network, the existing fault diagnosis systems need to extract fault features, the process of fault modeling is complex, and it is difficult to expand the knowledge in the operation of the system. In this paper, the constructive neural network method is applied to analog circuit fault diagnosis, and good diagnosis results are obtained. In this paper, based on the spherical model of M 鈮，
本文编号：2480995
[Abstract]:The research on fault diagnosis of analog circuits has been studied for decades. Affected by element tolerance, nonlinear, temperature drift and other factors, this subject has always been a difficult and hot research topic. Among electronic devices, analog circuits account for a small proportion, but the fault problems occur the most. The reliability of electronic devices depends on the reliability of analog circuits to a large extent. Under the background of the increasing size of the circuit, the traditional method shows the limitations of weak data processing ability, long diagnosis time and complex diagnosis process. Artificial intelligence method, especially neural network method, provides a new research direction for it. It can adapt to nonlinear circuit diagnosis, does not rely on specific circuits, and reduces the difficulty of diagnosis, but it is still insufficient in diagnosis time, diagnosis accuracy, error correction and fault tolerance, and the modeling process is complex. The constructive neural network based on coverage theory is a new neural network method proposed in recent years. Compared with the traditional neural network, it has the advantages of simple modeling, good robustness and strong computing ability, and is suitable for massive data. The industrial application in complex environment and so on can greatly reduce the operation time. In this paper, based on the theory of neural network, the existing fault diagnosis systems need to extract fault features, the process of fault modeling is complex, and it is difficult to expand the knowledge in the operation of the system. In this paper, the constructive neural network method is applied to analog circuit fault diagnosis, and good diagnosis results are obtained. In this paper, based on the spherical model of M 鈮，
本文编号：2480995
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