Self-tuning routine alarm analysis of vibration signals in steam turbine generators

This paper presents a self-tuning framework for knowledge-based diagnosis of routine alarms in steam turbine generators. The techniques provide a novel basis for initialising and updating time series feature extraction parameters used in the automated decision support of vibration events due to operational transients. The data-driven nature of the algorithms allows for machine specific characteristics of individual turbines to be learned and reasoned about. The paper provides a case study illustrating the routine alarm paradigm and the applicability of systems using such techniques

Diagnosing and predicting wind turbine faults from SCADA data using support vector machines

Unscheduled or reactive maintenance on wind turbines due to component failure incurs significant downtime and, in turn, loss of revenue. To this end, it is important to be able to perform maintenance before it's needed. To date, a strong effort has been applied to developing Condition Monitoring Systems (CMSs) which rely on retrofitting expensive vibration or oil analysis sensors to the turbine. Instead, by performing complex analysis of existing data from the turbine's Supervisory Control and Data Acquisition (SCADA) system, valuable insights into turbine performance can be obtained at a much lower cost. In this paper, fault and alarm data from a turbine on the Southern coast of Ireland is analysed to identify periods of nominal and faulty operation. Classification techniques are then applied to detect and diagnose faults by taking into account other SCADA data such as temperature, pitch and rotor data. This is then extended to allow prediction and diagnosis in advance of specific faults. Results are provided which show recall scores generally above 80\% for fault detection and diagnosis, and prediction up to 24 hours in advance of specific faults, representing significant improvement over previous techniques

Diagnostics and prognostics utilising dynamic Bayesian networks applied to a wind turbine gearbox

The UK has the largest installed capacity of offshore wind and this is set to increase significantly in future years. The difficulty in conducting maintenance offshore leads to increased operation and maintenance costs compared to onshore but with better condition monitoring and preventative maintenance strategies these costs could be reduced. In this paper an on-line condition monitoring system is created that is capable of diagnosing machine component conditions based on an array of sensor readings. It then informs the operator of actions required. This simplifies the role of the operator and the actions required can be optimised within the program to minimise costs. The program has been applied to a gearbox oil testbed to demonstrate its operational suitability. In addition a method for determining the most cost effective maintenance strategy is examined. This method uses a Dynamic Bayesian Network to simulate the degradation of wind turbine components, effectively acting as a prognostics tool, and calculates the cost of various preventative maintenance strategies compared to purely corrective maintenance actions. These methods are shown to reduce the cost of operating wind turbines in the offshore environment

Predictive maintenance with industrial sensor data

The Norwegian Ministry of Petroleum and Energy Commissions report shows that the government is making a large step closer to its ambition of allocating regions for 30,000 MW offshore wind via way of means of 2040. According to a report by IRENA, offshore wind operation and maintenance (O & M) costs make up a significant portion of the overall cost of electricity for offshore wind farms in G20 countries, ranging from 16-25%. To address this issue, it is essential to explore methods for improving operational reliability and reducing the maintenance costs of wind turbines. One promising approach is predictive maintenance, which involves leveraging data collected from sensors already equipped with the turbines to detect and address potential issues before they become more serious. Predictive maintenance is important in wind farms to reduce downtime and optimize the performance of wind turbines. Various rotating components in wind turbines make them complicated machinery, and if any of those parts fails, it can cause the entire turbine to shut down. This can result in lost revenue for the wind farm operator and lead to higher maintenance costs if the problem is not addressed quickly. This can be possible through a Supervisory Control and Data Acquisition (SCADA) system, which collects and analyzes data from various turbine components. We have developed a method for detecting and monitoring failures in critical components such as the gearbox and generator, based on historical SCADA data. Our approach utilizes machine learning models, specifically extreme gradient boosting (XGBoost), and has been tested on two real-world case studies involving eight different turbines. The outcomes show both the effectiveness and usefulness of our technique for boosting wind turbine reliability and minimizing maintenance costs

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

Degrader Analysis for Diagnostic and Predictive Capabilities: A Demonstration of Progress in DoD CBM+ Initiatives

This paper presents a modified reliability centered maintenance (RCM) methodology developed by The Applied Research Laboratory at The Pennsylvania State University (ARL Penn State) to meet challenges in decreasing life cycle sustainment costs for critical Naval assets. The focus of this paper is on the requirements for the development of the on-board Prognostics and Health Management (PHM) system with a discussion on the implementation progress for two systems: the high pressure air compressor (HPAC), and the advanced carbon dioxide removal unit (ACRU). Recent Department of Defense (DoD) guidance calls for implementing Condition Based Maintenance (CBM) as an alternative to traditional reactive and preventative maintenance strategies that rely on regular and active participation from subject matter experts to evaluate the health condition of critical systems. The RCM based degrader analysis utilizes data from multiple sources to provide a path for selecting systems and components most likely to benefit from the implementation of diagnostic and predictive capabilities for monitoring and managing failure modes by determining various options of possible CBM system designs that provide the highest potential ROI. Sensor data collected by the PHM system can be used with machine learning applications to develop failure mode predictive algorithms with greatest benefit in terms of performance, sustainment costs, and increasing platform operational availability. The approach supports traditional maintenance strategy development by assessing the financial benefit of the PHM technology implementation with promising potential for many industrial and military complex adaptive system applications