Reliability evaluation of Markov cyber–physical system oriented to cognition of equipment operating status
Introduction
Cyber–physical systems are systems that integrate the dynamic characteristics of physical processes with the software and communication technologies involved in these physical processes [1], [2], [3]. These systems demonstrate the connection between network information technology and the physical world. According to different application backgrounds, this type of system is also called process control system, monitoring control and data collection system or network control system [4], [5]. The application of cyber–physical system in real life determines its safety is very important. Once its safety has problems, it will cause huge irreversible damage to the corresponding controlled system [6]. For example, in the distributed management and control system in the power grid, water network, and natural gas network system, it is the core part in the health care, weapon system and transportation management system [7]. Once these cyber–physical systems have problems, they will have a major impact on public safety, health and even the national economy [8].
Condition-Based Monitoring (CBM) essentially provides a basis for fault diagnosis, performance evaluation and life prediction through real-time monitoring and processing of equipment operating conditions, so as to formulate corresponding maintenance strategies to prevent failures while avoiding unnecessary maintenance costs. CBM systems usually contain multiple functional modules, such as data acquisition, signal processing, feature extraction and selection, condition monitoring and health assessment, fault diagnosis, life prediction, and decision reasoning. The collection of real-time operating data such as vibration, pressure, and temperature reflecting the operating status of the equipment is completed by various sensors, but the data collected by the sensor not only reflects the useful information of the equipment status, but also includes transmission errors, measurement errors and environmental noise. Therefore, the data needs to be processed before the actual diagnosis and analysis. This processing method is also called signal processing. Various signal processing technologies can analyze and interpret the collected data, remove noise interference, and improve the signal to noise of the original data. Using these characteristics, the establishment of equipment failure models and performance prediction models can diagnose the operating status of the equipment in real time, evaluate its performance status and predict the remaining life of the equipment. Fault diagnosis and prediction are two important components of CBM: fault diagnosis realizes the fault detection of monitoring equipment, the determination of the fault location when the fault occurs, and the identification of the fault type; and the fault prediction determines the equipment based on the current monitoring data of the equipment.
This article introduces the basic concepts of Markov chain and HMM, and discusses the three basic problems of HMM: evaluation problem, decoding problem and learning problem. The basic algorithms to solve these three problems are introduced: Forward-Back algorithm, Viterbi algorithm and Baum–Welch algorithm. Specifically, the technical contributions of this article can be summarized as follows:
First: This article discusses the problems that may occur in the actual use of the HMM algorithm and provides solutions, and analyzes the reliability of the cyber–physical system under the random attack strategy.
Second: This article studies and analyzes the cascading failure process of three types of cyber–physical systems. Based on the theory of percolation in network science, the cascading failure process of the cyber–physical system under random attacks is systematically analyzed, and the iterative relationship of the cascading failure process is obtained, and the critical threshold of the cyber–physical system is obtained.
Third: We verified the calculated theoretical threshold and analyzed the relationship between the reliability of the cyber–physical system and network parameters.
The rest of this article is organized as follows. Section 2 discusses the theoretical basis of the reliability assessment of cyber–physical systems. Section 3 establishes a hidden Markov model for cognition of equipment operating status. Section 4 analyzes the experimental results. Section 5 summarizes the full text.
Section snippets
Related work
In the early development of cyber–physical systems, there was a lack of security protection considerations in the information domain [9]. With the widespread application of CPS, there are more and more industry fields for data circulation at all levels of the system, and the importance of CPS information security has become increasingly prominent. Unlike traditional IT systems, CPS emphasizes confidentiality and integrity in the security of the information domain [10]. The security of the
Reliability assessment method of equipment operating state
The reliability of equipment operating status is mainly to evaluate the sufficiency of equipment operating status, and quantitatively evaluate by calculating reliability indicators [21], [22]. According to the different evaluation objects, the reliability indexes of equipment operation status can be divided into load point indexes and system indexes. The load point index is used to characterize the reliability of a single load point to analyze the reliability of users in the system; the system
Hidden Markov model
The Markov process is a random process without aftereffect, in which the random process under the condition that the “now” state is known, the “future” state of the process is only related to the current state, but is related to the “past” state.
Given a random process , , for any , the probability value satisfies:
Then we call a Markov process. The random sequence S is called a Markov sequence, and the conditional distribution function P is often
Simulation verification
Since the cascading failure process for the three types of cyber–physical systems is exactly the same, as long as it is verified that the critical threshold of one type of cyber–physical system is correct, the other two must be correct, so we only choose one type for simulation experiment verification. Next, we mainly verify the first type of cyber–physical system.
We assume that the cyber–physical system is composed of two networks with different numbers of nodes. The two networks that make up
Conclusion
This paper counts the duration of the system state, calculates the load point and system reliability indicators. Aiming at the supporting role of the information system on the operation control of the equipment operating state, the topology structure of the information system and the overall structure of the communication network are introduced. Based on the function of the information system to transmit data information and control commands, a network connectivity model and information
CRediT authorship contribution statement
Qin Zhang: Performed the validation. Yutang Liu: Wrote the manuscript.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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