A comparative analysis of solar photovoltaic advanced fault detection and monitoring techniques

The non-linear I-V characteristics of the photovoltaic output have affected fault detection methods to work accurately. This scenario can cause hidden faults in the system and reduces overall productivity. Fault detection and monitoring techniques are evolving in photovoltaic fault management systems. Until recently, model-based technique, output signal analysis technique, statistically based technique, and machine learning techniques are the four main advanced fault detection methods that researchers have widely studied. This study has identified the limitations and advantages of previous photovoltaic fault detection and monitoring techniques, especially their applicability to all sizes of photovoltaic systems. This study proposes a multi-scale dual-stage photovoltaic fault detection and monitoring technique for better system safety, efficiency, and reliability. Challenges and suggestions for future research directions are also provided in this study. Overall, this study shall provide researchers and policymakers with a valuable reference for developing better fault detection and monitoring techniques for photovoltaic systems

A Review of Diagnostic Techniques for ISHM Applications

System diagnosis is an integral part of any Integrated System Health Management application. Diagnostic applications make use of system information from the design phase, such as safety and mission assurance analysis, failure modes and effects analysis, hazards analysis, functional models, fault propagation models, and testability analysis. In modern process control and equipment monitoring systems, topological and analytic , models of the nominal system, derived from design documents, are also employed for fault isolation and identification. Depending on the complexity of the monitored signals from the physical system, diagnostic applications may involve straightforward trending and feature extraction techniques to retrieve the parameters of importance from the sensor streams. They also may involve very complex analysis routines, such as signal processing, learning or classification methods to derive the parameters of importance to diagnosis. The process that is used to diagnose anomalous conditions from monitored system signals varies widely across the different approaches to system diagnosis. Rule-based expert systems, case-based reasoning systems, model-based reasoning systems, learning systems, and probabilistic reasoning systems are examples of the many diverse approaches ta diagnostic reasoning. Many engineering disciplines have specific approaches to modeling, monitoring and diagnosing anomalous conditions. Therefore, there is no "one-size-fits-all" approach to building diagnostic and health monitoring capabilities for a system. For instance, the conventional approaches to diagnosing failures in rotorcraft applications are very different from those used in communications systems. Further, online and offline automated diagnostic applications are integrated into an operations framework with flight crews, flight controllers and maintenance teams. While the emphasis of this paper is automation of health management functions, striking the correct balance between automated and human-performed tasks is a vital concern

Adaptive Monitoring of Complex Software Systems using Management Metrics

Software systems supporting networked, transaction-oriented services are large and complex; they comprise a multitude of inter-dependent layers and components, and they implement many dynamic optimization mechanisms. In addition, these systems are subject to workload that is hard to predict. These factors make monitoring these systems as well as performing problem determination challenging and costly. In this thesis we tackle these challenges with the goal of lowering the cost and improving the effectiveness of monitoring and problem determination by reducing the dependence on human operators. Specifically, this thesis presents and demonstrates the effectiveness of an efficient, automated monitoring approach which enables detection of errors and failures, and which assists in localizing faults. Software systems expose various types of monitoring data; this thesis focuses on the use of management metrics to monitor a system's health. We devise a system modeling approach which entails modeling stable, statistical correlations among management metrics; these correlations characterize a system's normal behaviour This approach allows a system model to be built automatically and efficiently using the monitoring data alone. In order to control the monitoring overhead, and yet allow a system's health to be assessed reliably, we design an adaptive monitoring approach. This adaptive capability builds on the flexible nature of our system modeling approach, which allows the set of monitored metrics to be altered at runtime. We develop methods to automatically select management metrics to collect at the minimal monitoring level, without any domain knowledge. In addition, we devise an automated fault localization approach, which leverages the ability of the monitoring system to analyze individual metrics. Using a realistic, multi-tier software system, including different applications based on Java Enterprise Edition and industrial-strength products, we evaluate our system modeling approach. We show that stable metric correlations exist in complex software systems and that many of these correlations can be modeled using simple, efficient techniques. We investigate the effect of the collection of management metrics on system performance. We show that the monitoring overhead can be high and thus needs to be controlled. We employ fault injection experiments to evaluate the effectiveness of our adaptive monitoring and fault localization approach. We demonstrate that our approach is cost-effective, has high fault coverage and, in the majority of the cases studied, provides pertinent diagnosis information. The main contribution of this work is to show how to monitor complex software systems and determine problems in them automatically and efficiently. Our solution approach has wide applicability and the techniques we use are simple and yet effective. Our work suggests that the cost of monitoring software systems is not necessarily a function of their complexity, providing hope that the health of increasingly large and complex systems can be tracked with a limited amount of human resources and without sacrificing much system performance

Review of recent research towards power cable life cycle management

Power cables are integral to modern urban power transmission and distribution systems. For power cable asset managers worldwide, a major challenge is how to manage effectively the expensive and vast network of cables, many of which are approaching, or have past, their design life. This study provides an in-depth review of recent research and development in cable failure analysis, condition monitoring and diagnosis, life assessment methods, fault location, and optimisation of maintenance and replacement strategies. These topics are essential to cable life cycle management (LCM), which aims to maximise the operational value of cable assets and is now being implemented in many power utility companies. The review expands on material presented at the 2015 JiCable conference and incorporates other recent publications. The review concludes that the full potential of cable condition monitoring, condition and life assessment has not fully realised. It is proposed that a combination of physics-based life modelling and statistical approaches, giving consideration to practical condition monitoring results and insulation response to in-service stress factors and short term stresses, such as water ingress, mechanical damage and imperfections left from manufacturing and installation processes, will be key to success in improved LCM of the vast amount of cable assets around the world

Experimental set-up for investigation of fault diagnosis of a centrifugal pump

Centrifugal pumps are complex machines which can experience different types of fault. Condition monitoring can be used in centrifugal pump fault detection through vibration analysis for mechanical and hydraulic forces. Vibration analysis methods have the potential to be combined with artificial intelligence systems where an automatic diagnostic method can be approached. An automatic fault diagnosis approach could be a good option to minimize human error and to provide a precise machine fault classification. This work aims to introduce an approach to centrifugal pump fault diagnosis based on artificial intelligence and genetic algorithm systems. An overview of the future works, research methodology and proposed experimental setup is presented and discussed. The expected results and outcomes based on the experimental work are illustrated

Embedding runtime verification post-deployment for real-time health management of safety-critical systems

As cyber-physical systems increase in both complexity and criticality, formal methods have gained traction for design-time verification of safety properties. A lightweight formal method, runtime verification (RV), embeds checks necessary for safety-critical system health management; however, these techniques have been slow to appear in practice despite repeated calls by both industry and academia to leverage them. Additionally, the state-of-the-art in RV lacks a best practice approach when a deployed system requires increased flexibility due to a change in mission, or in response to an emergent condition not accounted for at design time. Human-robot interaction necessitates stringent safety guarantees to protect humans sharing the workspace, particularly in hazardous environments. For example, Robonaut2 (R2) developed an emergent fault while deployed to the International Space Station. Possibly-inaccurate actuator readings trigger the R2 safety system, preventing further motion of a joint until a ground-control operator determines the root-cause and initiates proper corrective action. Operator time is scarce and expensive; when waiting, R2 is an obstacle instead of an asset. We adapt the Realizable, Responsive, Unobtrusive Unit (R2U2) RV framework for resource-constrained environments. We retrofit the R2 motor controller, embedding R2U2 within the remaining resources of the Field-Programmable Gate Array (FPGA) controlling the joint actuator. We add online, stream-based, real-time system health monitoring in a provably unobtrusive way that does not interfere with the control of the joint. We design and embed formal temporal logic specifications that disambiguate the emergent faults and enable automated corrective actions. We overview the challenges and techniques for formally specifying behaviors of an existing command and data bus. We present our specification debugging, validation, and refinement steps. We demonstrate success in the Robonaut2 case study, then detail effective techniques and lessons learned from adding RV with real-time fault disambiguation under the constraints of a deployed system

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Wind turbine condition monitoring : technical and commercial challenges.

Deployment of larger scale wind turbine systems, particularly offshore, requires more organized operation and maintenance strategies to ensure systems are safe, profitable and cost-effective. Among existing maintenance strategies, reliability centred maintenance is regarded as best for offshore wind turbines, delivering corrective and proactive (i.e. preventive and predictive) maintenance techniques enabling wind turbines to achieve high availability and low cost of energy. Reliability centred maintenance analysis may demonstrate that an accurate and reliable condition monitoring system is one method to increase availability and decrease the cost of energy from wind. In recent years, efforts have been made to develop efficient and cost-effective condition monitoring techniques for wind turbines. A number of commercial wind turbine monitoring systems are available in the market, most based on existing techniques from other rotating machine industries. Other wind turbine condition monitoring reviews have been published but have not addressed the technical and commercial challenges, in particular, reliability and value for money. The purpose of this paper is to fill this gap and present the wind industry with a detailed analysis of the current practical challenges with existing wind turbine condition monitoring technology