Verification of critical software is a high priority but a challenging task for industrial control systems. Model checking appears to be an appropriate approach for this purpose. However, this technique is not widely used in industry yet, due to some obstacles. The main obstacles encountered when trying to apply formal verification techniques at industrial installations are the difficulty of creating models out of PLC programs and defining formally the specification requirements. In addition, models produced out of real-life programs have a huge state space, thus preventing the verification due to performance issues. Our work at CERN (European Organization for Nuclear Research) focuses on developing efficient automatic verification methods for industrial critical installations based on PLC (Programmable Logic Controller) control systems. In this paper, we present a tool generating automatically formal models out of PLC code. The tool implements a general methodology which can support several input languages, like the PLC programming languages defined in the IEC 61131 standard, as well as the model formalisms of different model checker tools. The tool supports the three main stages of model checking: system modelization, requirement formalization and counterexample analysis. In addition, a verification case study of a PLC program, written in Structured Text (ST) language implemented at CERN is described. The paper shows that the verification process is automatized and supported by the proposed tool, thus its difficulty is completely hidden for the control engineer.

Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks) also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines.

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Abstract. Formal verification has become a recommended practice in the safety-critical application areas. However, due to the complexity of practical control and safety systems, the state space explosion often prevents the use of formal analysis. In this paper we extend our former verification methodology with effective property preserving reduction techniques. For this purpose we developed general rule-based reductions and a customized version of the Cone of Influence (COI) reduction. Using these methods, the verification of complex requirements formalised with temporal logics (e.g. CTL, LTL) can be orders of magnitude faster. We use the NuSMV model checker on a real-life PLC program from CERN to demonstrate the performance of our reduction techniques.

Abstract-Programmable Logic Controllers (PLCs) are embedded computers widely used in industrial control systems. Ensuring that a PLC software complies with its specification is a challenging task. Formal verification has become a recommended practice to ensure the correctness of safety-critical software but is still underused in industry due to the complexity of building and managing formal models of real applications. In this paper, we propose a general methodology to perform automated model checking of complex properties expressed in temporal logics (e.g., CTL, LTL) on PLC programs. This methodology is based on an Intermediate Model (IM), meant to transform PLC programs written in various standard languages (ST, SFC, etc.) to different modeling languages of verification tools. We present the syntax and semantics of the IM and the transformation rules of the ST and SFC languages to the nuXmv model checker passing through the intermediate model. Finally, two real cases studies of CERN PLC programs, written mainly in the ST language, are presented to illustrate and validate the proposed approach.