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

Prognostics and health management oriented data analytics suite for transformer health monitoring

Condition monitoring of power transformers is crucial for the reliable and cost-effective operation of the power grid. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety, and economic effects. With the advance of machine learning techniques, it is possible to enhance traditional transformer health monitoring techniques with data-driven and expert-based prognostics and health management (PHM) applications. Accordingly, this paper reviews the experience of the authors in the implementation of machine learning methods for transformer condition monitoring

Prognostics and health management oriented data analytics suite for transformer health monitoring

Condition monitoring of power transformers is crucial for the reliable and cost-effective operation of the power grid. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety, and economic effects. With the advance of machine learning techniques, it is possible to enhance traditional transformer health monitoring techniques with data-driven and expert-based prognostics and health management (PHM) applications. Accordingly, this paper reviews the experience of the authors in the implementation of machine learning methods for transformer condition monitoring

Towards a data analytics framework for medium voltage power cable lifetime mangement

Power cables are critical assets for the reliable and cost-effective operation of nuclear power plants. The unexpected failure of a power cable can lead to lack of export capability or even to catastrophic failures depending on the plant response to the cable failure and associated circuit. Prognostics and health management (PHM) strategies examine the health of the cable periodically to identify early indicators of anomalies, diagnose faults, and predict the remaining useful life. Traditionally, PHM-related strategies for power cables are considered separately with the associated penalties involved with this decision. Namely, there is a lack of consideration of the interactions and correlations between failure modes and PHM tests, which results in scalability issues of ad-hoc experiments, and accordingly incapability to exploit the full potential for PHM strategies in an effective manner. An effective and flexible PHM strategy should be able to consider not only different PHM strategies independently, but also it should be able to fuse these tests into a cable health state indicator. The main contribution of this paper is the proposal of a PHM-oriented data analytics framework for medium voltage power cable lifetime management which incorporates anomaly detection, diagnostics, prognostics, and health index modules. This framework includes the characterization of existing data sources and PHM-oriented data analytics for cable condition monitoring. This process enables the creation of a database of existing datasets, identification of complementary PHM techniques for an improved condition monitoring, and implementation of an end-to-end PHM framework

Probabilistic machine learning aided transformer lifetime prediction framework for wind energy systems

Accurate lifetime prediction of transformers operated in power grids with renewable energy systems is a challenging task because it requires a large amount of data that is not usually available. In the case of wind energy, this complexity is intensified with the stochastic ageing process influenced by the intermittency of the wind and weather conditions. Existing models make use of detailed power topologies to evaluate transformer stress profiles and associated degradation. However, this modelling approach requires high computational resources and long simulation times. In this context, this paper presents a lifetime prediction model for transformers designed through probabilistic machine learning, thermal modelling and ageing analysis. The proposed model is compared with synthetic wind-to-power detailed simulations of a wind farm and validated with real data. The lifetime prediction is evaluated with different mission profile estimates and results show that the accuracy of the probabilistic machine learning model is very high, with an error of 0.47% for the median value and 80% prediction interval errors within 6%–7% with respect to observations. Moreover, there is a substantial reduction in the simulation time and memory requirements when compared to the synthetic model. A detailed sensitivity analysis demonstrates the influence on transformer ageing of different overloading strategies, thermal constants and the geographic location of the wind farm

Introducing Axial Chirality into Mesoionic 4,4′-Bis(1,2,3-triazole) Dicarbenes

Mesoionic 4,4′-bis(1,2,3-triazole-5,5′-diylidene) Rh(I) complexes having a C2 chiral 4,4′-axis were accessed from 3-alkyltriazolium salts in virtually complete de. Their structure and configurational integrity were assessed by NMR spectroscopy, X-ray crystallography, and chiral HPLC. Computational analysis of the MICs involved in the reaction suggested the formation of a highly stable and unprecedented cation-carbene intermediate species, which could be evidenced experimentally by cyclic voltammetry analysis

Introducing Axial Chirality into Mesoionic 4,4′-Bis(1,2,3-triazole) Dicarbenes

Mesoionic 4,4′-bis(1,2,3-triazole-5,5′-diylidene) Rh(I) complexes having a C2 chiral 4,4′-axis were accessed from 3-alkyltriazolium salts in virtually complete de. Their structure and configurational integrity were assessed by NMR spectroscopy, X-ray crystallography, and chiral HPLC. Computational analysis of the MICs involved in the reaction suggested the formation of a highly stable and unprecedented cation-carbene intermediate species, which could be evidenced experimentally by cyclic voltammetry analysis