Development of Fault Diagnostics, and Prognosis System based on Digital Twin and Blockchain

Abstract

The black box of airplanes created increasing interest for several decades, especially for its role in identifying aviation accidents. A black box can be defined as a base which records the operation and the exchanges between the switches and the pilots. However, there are less chances to recuperate the black box for each accident. Therefore, in this paper, a design of a system which could help to diagnostic and obtaining propositions of solutions is proposed. The system is able to provide information at the simultaneously as it is operating on it virtual twin or representation of the machine. For retaining the data, the concept of incorporating capabilities of block chain is used. Explicitly, the system is split into two parts, the real-time digital twin (DT) and the copy of digital twin. The real DT is composed of the actual machine, data collecting sensors, data processing and analysis units, and the virtual twin of the real machine. The second part of the system is composed of several independent databases, data processing and analysis sections, and its virtual twin. The purpose of this system is to let the DT to be easily accessed from anywhere in the world. With this, the system can retain the lost data, and retained data can help to reconstitute accident situation using simulation. Moreover, machine dysfunction issues can be identified and information can be used while manufacturing new systems. To achieve these tasks, substantial practical knowledge of operation of fault diagnostics and prognosis systems, concepts of signal processing, digital twin, and block chain is essential.

In this paper, initially various details of prerequisites required by an engineer to conduct machinery condition monitoring is discussed. This include concepts of instrumentation, signal processing techniques such as vibration monitoring, motor volume signature analysis, and thermography where debris analysis and detection techniques are discussed. Furthermore, principles on how to operate maintenance operations in particular conditions illustrating the best techniques available for making a good maintenance decisions known as FEMCA (Failure Mode, Effect and Criticality Analysis) are described.

The concept of prognosis will help us to determine the remaining predictable lifespan of the machine or the machine components using mathematical modelling and machine learning approaches. Briefly, we will prove how machine learning can help to make a full-proof system as to how other fault can be diagnosed [1]. In the past, several researchers focused on works on diagnostics, predictive maintenance, digital twin, and block chain. In [2], researchers developed a simplified software model which can simulate the digital twin for the application of diagnosing the status of a power transformer. In [3], researchers designed and implemented block chain based creation process for digital twin for guaranteeing security, trust, accessibility, and data provenance. They have created this process having characteristics of decentralized, tamper-proof, and immutable features. In [4], the details on recommending precautionary measures ahead of performing critical events by identifying the faults is studied. Here the role of integrating block chain and digital twin for fault diagnosis is discussed as a key challenge.

The overview of the implementation digital twin and block chain for the proposed system is shown in Fig.1, with fundamental objectives of achieving confidentiality, accessibility, and avoiding any possibility of alteration of transactions etc.