A Cloud-Based Decision Support System for Self-Healing in Distributed Automation Systems Using Fault Tree Analysis

Abstract

Abstract-Downtime is a key performance index for industrial automation systems. An industrial automation system achieves maximum productivity when its downtime is reduced to the minimum. One approach to minimize downtime is to predict system faults and recover from them automatically. A cloud-based decision support system is proposed for rapid problem identifications and to assist the self-management processes. By running multiple parallel simulations of control software with real-time inputs ahead of system time, faults could be detected and corrected automatically using autonomous industrial software agents. Fault trees, as well as control algorithms, are modeled using IEC 61499 function blocks that can be directly executed on both physical controllers and cloud services. A case study of water heating process is used to demonstrate the self-healing process supported by the cloud-based decision support system. Index Terms-Cloud-based decision support systems, distributed automation systems, faster-than-real-time simulation, fault tree analysis (FTA), IEC 61499 function blocks (FBs), programmable logic controllers, self-healing, supervisory control.

Data Management in Industry 4.0: State of the Art and Open Challenges

“…The first category concerns deploying manufacturing software on local or global clouds, i.e., a "manufacturing version" of cloud computing. The second category has a broader scope, [409] manufacturing resources global Assembly Line [402] manufacturing services Assembly Line [403] shared memories WSAN [401] mobile network nodes NCS [406] network services NCS [405] virtual resources hybrid IIoT [407] network devices local cutting across production, management, design and engineering abilities in a manufacturing business. Unlike with classic computing and data storage, manufacturing involves physical equipment, monitors, materials and so on.…”

“…In [401], the authors consider industrial WSAN with mobile nodes and propose a fixedpath mobile node handover strategy, assisted by cloud services and an ants-colony algorithm. In [406], the authors propose a cloud-based decision support system for self-healing in distributed automation systems using fault tree analysis. Some fewer recent works employ hybrid or local clouds.…”

A Method to Extract Causality for Safety Events in Chemical Accidents from Fault Trees and Accident Reports

“…(3) Achieve word segmentation for ARS by the tool "Jieba" and build the corpus CA; (4) For each word w in CA, train a vector for w by "Word2vec"; (5) For each ft ∈ FTS (6) For each n e ∈ ft.E (7) {Identify the event e in the node n e ; (8) ES � ES ∪ {e}; } (9) For ∀e i and e j ∈ ES, build event pair <e i , e j > and EPS � EPS ∪ {<e i , e j >}; (10) For each <e i , e j >∈ EPS (11) If ∃g ∈ ft.G, s.t. : e i ∈ I(g)∧e j ∈ O(g) or e j ∈ I(g)∧e i ∈ O(g) then ECS � ECS ∪ {<e i , e j >}; (12) Else { construct ISFe ij and use ve represent the vector of ISFe ij (13) compute Se ij ; (14) For each Se p sentence in Se ij (15) Build the vector sp for Se p , S p � Σα it * h it ; (16) Generate the vector vs. for…”

“…Four state-of-the-art methods including Feature-SVM (F-SVM) [8], BiLSTM [37], pattern-argument semantics (P-A S) [38], and Multicolumn CNN (MCCNN) [39] were also executed on the same dataset to obtain causality. As shown in Figure 7, the accuracy and recall rate of our method is the highest one.…”

“…Fault tree analysis is one of the most frequently used methods in safety analysis, prevention, and emergency disposal [7]. Fault trees can describe the causes of accidents and their temporal logic relationship [8]. So, we can find out the key events in chemical accidents and predict potential hazards in chemical production from the existing fault trees.…”