Sequential fault diagnosis is an approach that realizes fault isolation by executing the optimal test step by step. The strategy used, i.e., the sequential diagnostic strategy, has great influence on diagnostic accuracy and cost. Optimal sequential diagnostic strategy generation is an important step in the process of diagnosis system construction, which has been studied extensively in the literature. However, previous algorithms either are designed for single mode systems or do not consider test placement cost. They are not suitable to solve the sequential diagnostic strategy generation problem considering test placement cost for multimode systems. Therefore, this problem is studied in this paper. A formulation is presented. Two algorithms are proposed, one of which is realized by system transformation and the other is newly designed. Extensive simulations are carried out to test the effectiveness of the algorithms. A real-world system is also presented. All the results show that both of them have the ability to solve the diagnostic strategy generation problem, and they have different characteristics.

Previous test sequencing algorithms only consider the execution cost of a test at the application stage. Due to the fact that the placement cost of some tests at the design stage is considerably high compared with the execution cost, the sequential diagnosis strategy obtained by previous methods is actually not optimal from the view of life cycle. In this paper, the test sequencing problem based on life cycle cost is presented. It is formulated as an optimization problem, which is non-deterministic polynomial-time hard (NP-hard). An algorithm and a strategy to improve its computational efficiency are proposed. The formulation and algorithms are tested on various simulated systems and comparisons are made with the extant test sequencing methods. Application on a pump rotational speed control (PRSC) system of a spacecraft is studied in detail. Both the simulation results and the real-world case application results suggest that the solution proposed in this paper can significantly reduce the life cycle cost of a sequential fault diagnosis strategy.

Magnetically-coupled resonant wireless power transfer (MCR-WPT) technology is one of the most promising technologies to power embedded sensors. In many practical situations, embedded sensors are located in a metal environment. The nearby metal materials will affect the performance of the MCR-WPT system. In this paper, the equivalent circuit model of the MCR-WPT system in metal environment is established and the factors affecting the transmission efficiency is analyzed. Then, simulation analysis is performed using Maxwell software and Simplorer software. Two cases are considered in the simulation study: the coil is close to a metal plate and the coil is close to a metal shaft. Simulation results show that metal materials will change the coil inductance and interfere with the magnetic field distribution of the MCR-WPT system, which in turn affects the transmission efficiency of the system.

A delay dynamic coupled fault diagnosis (DDCFD) model is a kind of effective fault diagnosis model based on probability graph for complex systems. Previous research assumes that its structure and parameters are all known before application. Since the initial model is always constructed by experts, it is possible that it is inconsistent with practical systems, which may impact the diagnostic accuracy. In order to improve the model by applying the practical data, this paper develops a machine learning method for the DDCFD model. A parameter learning method and three structure learning methods are studied. Using this approach, prior knowledge and test data can be combined together to get a better diagnostic model. The methods are tested and compared on simulated examples and an application case. The result indicates that diagnostic accuracy can be improved by means of parameter learning compared to the initial model, and it can be further improved by structure learning. The score-based algorithm is the best choice for learning the structure of the DDCFD model.