Sensor selection plays an essential and fundamental role in prognostics and health management technology, and it is closely related to fault diagnosis, life prediction, and health assessment. The existing methods of sensor selection do not have an evaluation standard, which leads to different selection results. It is not helpful for the selection and layout of sensors. This paper proposes a comprehensive evaluation method of sensor selection for prognostics and health management (PHM) based on grey clustering. The described approach divides sensors into three grey classes, and defines and quantifies three grey indexes based on a dependency matrix. After a brief introduction to the whitening weight function, we propose a combination weight considering the objective data and subjective tendency to improve the effectiveness of the selection result. Finally, the clustering result of sensors is obtained by analyzing the clustering coefficient, which is calculated based on the grey clustering theory. The proposed approach is illustrated by an electronic control system, in which the effectiveness of different methods of sensor selection is compared. The result shows that the technique can give a convincing analysis result by evaluating the selection results of different methods, and is also very helpful for adjusting sensors to provide a more precise result. This approach can be utilized in sensor selection and evaluation for prognostics and health management.

The dependency matrix is widely used in fault diagnosis as a valid diagnostic model. However, the conventional dependency matrix-based diagnosis method pays more attention to the extension and improvement of the dependency matrix, and the timeliness is rarely discussed. To solve the problem and improve the diagnosis efficiency, fast diagnosis method using two search algorithms is proposed. The hash function is introduced, and the dependency matrix is transformed into the key-value pairs. The binary search algorithm is brought in to locate the desired failure mode by splitting the dependency matrix. Thus, the fault diagnosis is converted into a search process, which can improve the diagnosis efficiency by making full use of the advantages of the search algorithms. Finally, a numerical simulation is utilized to verify the proposed method. The proposed methods are applied to matrices of different sizes. The results show that the hash-based diagnosis method has significantly better efficiency, making it more suitable for onboard diagnosis. On the other hand, the binary search-based method is advantageous from a memory perspective only when the matrix size is small.

Built-in test (BIT) technology provides an effective way of state monitoring, and is widely used in many fields. However, the false alarm prevents its wider usage and reduces its reliability. The existing BIT methods typically assume that the probability of generating a false alarm is a stationary process, which is typically a too much simplifying assumption. In this paper, we propose a new method of false alarm recognition considering the time series characteristics of BIT. The presented method divides the evolution process of false alarm into three phases, and the false alarm recognition in the intermediate phase is discussed and studied. Multi-classification support-vector machine is introduced to identify the intermediate phase, and a new feature, the irrational entropy, is proposed as its input. Based on the phase identification result, we construct an HMM for every state: normal, false alarm, and faulty state. The state with the highest probability is taken as the true state. Finally, a DC-DC converter is taken as an example, and the proposed method is applied and compared with the repeated test method-artificial neural network (ANN), and recurrent neural network (RNN). The results show that the presented method outperforms the other algorithms.