عنوان انگلیسی مقاله:
Intelligent fault diagnosis for rotating machinery using deep Q-network based health state classification: A deep reinforcement learning approach
ترجمه فارسی عنوان مقاله:
تشخیص خطای هوشمند برای ماشین آلات در حال چرخش با استفاده از طبقه بندی حالت سلامت مبتنی بر شبکه Q عمقی: یک روش یادگیری تقویتی عمیق
Sciencedirect - Elsevier - Advanced Engineering Informatics, 42 (2019) 100977: doi:10:1016/j:aei:2019:100977
Yu Dinga,b, Liang Maa,b, Jian Maa,b, Mingliang Suoa,b, Laifa Taoa,b, Yujie Chenga,b, Chen Lua,b,⁎
Fault diagnosis methods for rotating machinery have always been a hot research topic, and artificial intelligencebased
approaches have attracted increasing attention from both researchers and engineers. Among those related
studies and methods, artificial neural networks, especially deep learning-based methods, are widely used to
extract fault features or classify fault features obtained by other signal processing techniques. Although such
methods could solve the fault diagnosis problems of rotating machinery, there are still two deficiencies. (1)
Unable to establish direct linear or non-linear mapping between raw data and the corresponding fault modes, the
performance of such fault diagnosis methods highly depends on the quality of the extracted features. (2) The
optimization of neural network architecture and parameters, especially for deep neural networks, requires
considerable manual modification and expert experience, which limits the applicability and generalization of
such methods. As a remarkable breakthrough in artificial intelligence, AlphaGo, a representative achievement of
deep reinforcement learning, provides inspiration and direction for the aforementioned shortcomings.
Combining the advantages of deep learning and reinforcement learning, deep reinforcement learning is able to
build an end-to-end fault diagnosis architecture that can directly map raw fault data to the corresponding fault
modes. Thus, based on deep reinforcement learning, a novel intelligent diagnosis method is proposed that is able
to overcome the shortcomings of the aforementioned diagnosis methods. Validation tests of the proposed method
are carried out using datasets of two types of rotating machinery, rolling bearings and hydraulic pumps, which
contain a large number of measured raw vibration signals under different health states and working conditions.
The diagnosis results show that the proposed method is able to obtain intelligent fault diagnosis agents that can
mine the relationships between the raw vibration signals and fault modes autonomously and effectively.
Considering that the learning process of the proposed method depends only on the replayed memories of the
agent and the overall rewards, which represent much weaker feedback than that obtained by the supervised
learning-based method, the proposed method is promising in establishing a general fault diagnosis architecture
for rotating machinery.
Keywords: Fault diagnosis | Rotating machinery | Deep reinforcement learning | Deep Q-network