Fault detection for discrete event systems using Petri nets with unobservable transitions

In this paper we present a fault detection approach for discrete event systems using Petri nets. We assume that some of the transitions of the net are unobservable, including all those transitions that model faulty behaviors. Our diagnosis approach is based on the notions of basis marking and justification, that allow us to characterize the set of markings that are consistent with the actual observation, and the set of unobservable transitions whose firing enable it. This approach applies to all net systems whose unobservable subnet is acyclic. If the net system is also bounded the proposed approach may be significantly simplified by moving the most burdensome part of the procedure off-line, thanks to the construction of a graph, called the basis reachability graph

Diagnosability Analysis of Labeled Time Petri Net Systems

In this paper, we focus on two notions of diagnosability for labeled Time Petri net (PN) systems: K-diagnosability implies that any fault occurrence can be detected after at most K observations, while τ-diagnosability implies that any fault occurrence can be detected after at most τ time units. A procedure to analyze such properties isprovided.The proposedapproach uses the Modified State Class Graph, a graph the authors recently introduced for the marking estimation of labeled Time PN systems,which providesan exhaustive description of the system behavior. A preliminary diagnosabilty analysis of the underlying logic system based on classical approaches taken from the literature is required. Then, the solution of some linear programming problems should be performed to take into account the timing constraints associated with transitions

A survey on efficient diagnosability tests for automata and bounded Petri nets

This paper presents a survey and evaluation of the efficiency of polynomial diagnosability algorithms for systems modeled by Petri nets and automata. A modified verification algorithm that reduces the state space by exploiting symmetry and abstracting unobservable transitions is also proposed. We show the importance of minimal explanations on the performance of diagnosability verifiers. Different verifiers are compared in terms of state space and elapsed time. It is shown that the minimal explanation notion involved in the modified basis reachability graph, a graph presented by Cabasino et al. [3] for diagnosability analysis of Petri nets, has great impact also on automata-based diagnosability methods. The evaluation often shows improved computation times of a factor 1000 or more when the concept of minimal explanation is included in the computation

Association of kidney disease measures with risk of renal function worsening in patients with type 1 diabetes

Background: Albuminuria has been classically considered a marker of kidney damage progression in diabetic patients and it is routinely assessed to monitor kidney function. However, the role of a mild GFR reduction on the development of stage 653 CKD has been less explored in type 1 diabetes mellitus (T1DM) patients. Aim of the present study was to evaluate the prognostic role of kidney disease measures, namely albuminuria and reduced GFR, on the development of stage 653 CKD in a large cohort of patients affected by T1DM. Methods: A total of 4284 patients affected by T1DM followed-up at 76 diabetes centers participating to the Italian Association of Clinical Diabetologists (Associazione Medici Diabetologi, AMD) initiative constitutes the study population. Urinary albumin excretion (ACR) and estimated GFR (eGFR) were retrieved and analyzed. The incidence of stage 653 CKD (eGFR < 60 mL/min/1.73 m2) or eGFR reduction > 30% from baseline was evaluated. Results: The mean estimated GFR was 98 \ub1 17 mL/min/1.73m2 and the proportion of patients with albuminuria was 15.3% (n = 654) at baseline. About 8% (n = 337) of patients developed one of the two renal endpoints during the 4-year follow-up period. Age, albuminuria (micro or macro) and baseline eGFR < 90 ml/min/m2 were independent risk factors for stage 653 CKD and renal function worsening. When compared to patients with eGFR > 90 ml/min/1.73m2 and normoalbuminuria, those with albuminuria at baseline had a 1.69 greater risk of reaching stage 3 CKD, while patients with mild eGFR reduction (i.e. eGFR between 90 and 60 mL/min/1.73 m2) show a 3.81 greater risk that rose to 8.24 for those patients with albuminuria and mild eGFR reduction at baseline. Conclusions: Albuminuria and eGFR reduction represent independent risk factors for incident stage 653 CKD in T1DM patients. The simultaneous occurrence of reduced eGFR and albuminuria have a synergistic effect on renal function worsening

Identification of Petri nets from knowledge of their language

In this paper we deal with the problem of identifying a Petri net system, given a finite language generated by it. First we consider the problem of identifying a free labeled Petri net system, i.e., all transition labels are distinct. The set of transitions and the number of places is assumed to be known, while the net structure and the initial marking are computed solving an integer programming problem. Then we extend this approach in several ways introducing additional information about the model (structural constraints, conservative components, stationary sequences) or about its initial marking. We also treat the problem of synthesizing a bounded net system starting from an automaton that generates its language. Finally, we show how the approach can also be generalized to the case of labeled Petri nets, where two or more transitions may share the same label. In particular, in this case we impose that the resulting net system is deterministic. In both cases the identification problem can still be solved via an integer programming problem

State Estimation and Fault Diagnosis of Labeled Time Petri Net Systems with Unobservable Transitions

In this paper, we present a procedure for the state estimation and fault diagnosis of a labeled Time Petri net system. Starting from the State Class Graph defined by Berthomieu and Diaz, we introduce a new graph called Modified State Class Graph (MSCG) that allows an exhaustive representation of the evolution of the timed system. Then, we present a procedure that, given a timed observation, i.e., a sequence of labels with their firing time instants, and a time instant τ , allows one to determine in which states the system can be at time τ by using the MSCG and solving a certain number of linear programming problems. Finally, we present a procedure to perform fault diagnosis using the MSCG