Complex systems are playing increasingly important roles in the modernization process, so it's necessary to improve their operational reliability. The improvement of system operational reliability can be achieved through faster and more accurate fault diagnosis and prognosis of key components in the system. Traditional machine learning methods and model-based methods have become difficult to effectively diagnose and predict faults, while deep learning, with its excellent data learning ability, has enormous potential in fault diagnosis and prognosis. This paper elaborates on the application research and development of four common deep learning models applied in the fields of fault diagnosis and prognosis: autoencoder and its variants, deep belief network, convolutional neural network and recurrent neural network. The advantages and limitations of various models of deep learning are discussed, and their possible future research directions are prospected.
In response to the conventional approach of designing reliability for redundant systems, which often assumes independence among component failures and may not fully capture the true system reliability, this study introduces a novel reliability simulation design methodology that accounts for the correlation of component failures. Initially, a reliability design model for redundant systems, incorporating component failure correlation, is formulated using the copula function. Subsequently, an innovative simulation solution algorithm, grounded in the generalized reduced gradient method model, is developed. Finally, the efficacy of the proposed model and algorithm is demonstrated through a numerical illustration. The outcomes underscore the pronounced disparities in system reliability design strategies when considering the interdependency of component failures. This research furnishes valuable insights into optimizing the reliability design of equipment redundancy systems.
This paper addresses the challenge of inadequate observation data for the reliability assessment of newly deployed wind turbines. It introduces a methodology for evaluating the reliability of key wind turbine components, grounded in multi-source data. By constructing a fusion algorithm of fault data and performance degradation data, using Monte Carlo sampling technology to realize unit fault data expansion and fusion. Based on the fusion data, distribution function primary, goodness of fit test, parameter estimation, and reliability estimation are carried out. Finally, the reliability of key components of the unit, MTBF, and reliable life of the point estimation and interval estimation. The effectiveness of the method is verified by taking a gearbox as an example.
To achieve the optimal reliability allocation scheme that meets the system reliability requirements while minimizing the cost, an improved particle swarm optimization (PSO) algorithm is proposed in this paper. The optimal allocation scheme is iteratively solved by taking the lowest total allocation cost as the objective function and taking into account the upper and lower limit constraints of component reliability and system reliability index constraints. This paper adopts the four parameter cost reliability model commonly used in engineering to establish an objective function. For the upper and lower limit constraints of component reliability, a correction link is added to the classical PSO algorithm to ensure that the particles generated in each iteration meet the component reliability constraints. For system reliability index constraints, a discard link is added to the classical PSO algorithm to ensure that each particle is a feasible solution. Finally, the above improved PSO algorithm is realized through a numerical example. And compared with other algorithms in the literature, it shows that the improved PSO algorithm can simultaneously obtain the optimization results of the lowest total allocation cost and the highest system reliability.
Electrical connectors are essential components in electrical systems. During their usage, frictional wear can occur between the pin and socket of the contact components. Excessive contact resistance resulting from this phenomenon can lead to system failure, emphasizing the critical importance of ensuring the stability of the contact component's performance. In this study, the commonly used slotted contact component was selected, and its mechanical characteristics were initially analyzed to determine the required parameters for experimentation. Subsequently, vibration tests under different vibration conditions were conducted on the contact component at room temperature, revealing the wear patterns of the contact component under various vibration conditions. Finally, a physical model for the failure of frictional wear was established for the selected contact component model based on the parameters obtained from the experiments.
