Jose Ignacio Aizpurua

On Cost-effective Reuse of Components in the Design of Complex Reconfigurable Systems

Design strategies that benefit from the reuse of system components can reduce costs whilst maintaining or increasing dependability—we use the term dependability to tie together reliability and availability. D3H2 (aDaptive Dependable Design for systems with Homogeneous and Heterogeneous redundancies) is a methodology that supports the design of complex systems with a focus on reconfiguration and component reuse. D3H2 systematises the identification of heterogeneous redundancies and optimises the design of fault detection and reconfiguration mechanisms, by enabling the analysis of design alternatives with respect to dependability and cost. In this paper, we extend D3H2 for application to repairable systems. The method is extended with analysis capabilities allowing dependability assessment of complex reconfigurable systems. Analysed scenarios include time-dependencies between failure events and the corresponding reconfiguration actions. We demonstrate how D3H2 can support decisions about fault detection and reconfiguration that seek to improve dependability whilst reducing costs via application to a realistic railway case study.

Uncertainty-Aware Dynamic Reliability Analysis Framework for Complex Systems

Critical technological systems exhibit complex dynamic characteristics such as time-dependent behavior, functional dependencies among events, sequencing and priority of causes that may alter the effects of failure. Dynamic fault trees (DFTs) have been used in the past to model the failure logic of such systems, but the quantitative analysis of DFTs has assumed the existence of precise failure data and statistical independence among events, which are unrealistic assumptions. In this paper, we propose an improved approach to reliability analysis of dynamic systems, allowing for uncertain failure data and statistical and stochastic dependencies among events. In the proposed framework, DFTs are used for dynamic failure modeling. Quantitative evaluation of DFTs is performed by converting them into generalized stochastic Petri nets. When failure data are unavailable, expert judgment and fuzzy set theory are used to obtain reasonable estimates. The approach is demonstrated on a simplified model of a cardiac assist system.

Coherence region of the Priority‐AND gate: Analytical and numerical examples

In recent years, the need for a more accurate dependability modelling (encompassing reliability, availability, maintenance, and safety) has favoured the emergence of novel dynamic dependability techniques able to account for temporal and stochastic dependencies of a system. One of the most successful and widely used methods is Dynamic Fault Tree that, with the introduction of the dynamic gates, enables the analysis of dynamic failure logic systems such as fault-tolerant or reconfigurable systems. Among the dynamic gates, Priority-AND (PAND) is one of the most frequently used gates for the specification and analysis of event sequences. Despite the numerous modelling contributions addressing the resolution of the PAND gate, its failure logic and the consequences for the coherence behaviour of the system need to be examined to understand its effects for engineering decision-making scenarios including design optimization and sensitivity analysis. Accordingly, the aim of this short communication is to analyse the coherence region of the PAND gate so as to determine the coherence bounds and improve the efficacy of the dynamic dependability modelling process.

Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry.

A model-based extension to HiP-HOPS for dynamic fault propagation studies

HiP-HOPS is a model-based approach for assessing the dependability of safety-critical systems. The method combines models, logic, probabilities and nature-inspired algorithms to provide advanced capabilities for design optimisation, requirement allocation and safety argument generation. To deal with dynamic systems, HiP-HOPS has introduced temporal operators and a temporal logic to represent and assess event sequences in component failure modelling. Although this approach has been shown to work, it is not entirely consistent with the way designers tend to express operational dynamics in models which show mode and state sequences. To align HiP-HOPS better with typical design techniques, in this paper, we extend the method with the ability to explicitly consider different modes of operation. With this added capability HiP-HOPS can create and analyse temporal fault trees from architectural models of a system which are augmented with mode information.

Multiple-person tracking devoted to distributed multi smart camera networks

Camera networks are an important component of modern complex systems, be it for surveillance, human/machine interaction or healthcare. Having smart cameras that can, by themselves, perform part of the data processing improves scalability both in processing and network resources. In this paper, we present the HYBRID algorithm for multiple person tracking intended for implementation on a smart camera platform, along with the development methodology to implement said algorithm in an FPGA-based smart camera. The HYBRID strategy outperforms the well-known Markov Chain Monte Carlo based particle filter (MCMC-PF) in terms of (i) parallelization capabilities as the MCMC-PF sequentially processes the particles, and (ii) tracking performances (i.e., robustness and precision).

A Model-Based Hybrid Approach for Circuit Breaker Prognostics Encompassing Dynamic Reliability and Uncertainty

Prognostics predictions estimate the remaining useful life (RUL) of assets. This information enables the implementation of condition-based maintenance strategies by scheduling intervention when failure is imminent. Circuit breakers (CBs) are key assets for the correct operation of the power network, fulfilling both a protection and a network reconfiguration role. Certain breakers will perform switching on a deterministic schedule, while operating stochastically in response to network faults. Both types of operation increase wear on the main contact, with high fault currents leading to more rapid aging. This paper presents a hybrid approach for prognostics of CBs, which integrates deterministic and stochastic operation through piecewise deterministic Markov processes. The main contributions of this paper are: 1) the integration of hybrid prognostics models with dynamic reliability concepts for a more accurate RUL forecasting and 2) the uncertain failure threshold modeling to integrate and propagate uncertain failure evaluation levels in the prognostics estimation process. Results show the effect of dynamic operation conditions on prognostics predictions and confirm the potential for its use within a condition-based maintenance strategy.

Improved Dynamic Dependability Assessment Through Integration With Prognostics

The use of average data for dependability assessments results in an outdated system-level dependability estimation, which can lead to incorrect design decisions. With increasing availability of online data, there is room to improve traditional dependability assessment techniques. Namely, prognostics is an emerging field, which provides asset-specific failure information that can be reused to improve the system-level failure estimation. This paper presents a framework for prognostics-updated dynamic dependability assessment. The dynamic behavior comes from runtime updated information, asset interdependencies, and time-dependent system behavior. A case study from the power generation industry is analyzed, and results confirm the validity of the approach for improved near real-time unavailability estimations.

Adaptive power transformer lifetime predictions through machine learning & uncertainty modelling in nuclear power plants

The remaining useful life (RUL) of transformer insulation paper is largely determined by the winding hot-spot temperature (HST). Frequently the HST is not directly monitored and it is inferred from other measurements. However, measurement errors affect prediction models and if uncertain variables are not taken into account this can lead to incorrect maintenance decisions. Additionally, existing analytic models for HST calculation are not always accurate because they cannot generalize the properties of transformers operating in different contexts. In this context, this paper presents a novel transformer condition assessment approach integrating uncertainty modeling, data-driven forecasting models, and model-based experimental models to increase the prediction accuracy and handle uncertainty. The proposed approach quantifies the effect of measurement errors on transformer RUL predictions and confirms that temperature and load measurement errors affect the RUL estimation. Forecasting results show that the extreme gradient boosting (XGB) algorithm best captures the nonlinearities of the thermal model and improves the prediction accuracy among a number of forecasting approaches. Accordingly, the XGB model is integrated with experimental models in a particle filtering framework to improve thermal modeling and RUL prediction tasks. Models are tested and validated using a real dataset from a power transformer operating in a nuclear power plant.

Improving the accuracy of transformer DGA diagnosis in the presence of conflicting evidence

Transformers are critical assets for the reliable and cost-effective operation of the power grid. Transformers may fail if condition monitoring does not identify degraded conditions in time. Dissolved Gas Analysis (DGA) focuses on the examination of the dissolved gasses in the transformer oil and there exist different methods for transformer fault diagnosis based on different analyses of the gassing levels. However, these methods can give conflicting results, and it is not always clear which model is most accurate in a given situation. This paper presents a novel evidence combination framework for DGA based on Bayesian networks. Bayesian network models embed expert knowledge along with learned data patterns and evidence combination which aids in the consistency of analysis. The effectiveness of the proposed framework is validated using the IEC TC 10 dataset with a maximum diagnosis accuracy of 88.3%.