Prognostics-driven supply chain optimization in commercial aviation

Maintenance providers in commercial aviation are looking into new strategies to improve maintenance operations and aircraft availability in order to reduce cost and increase customer satisfaction. Aircraft availability and thus profitability highly relies on adequate maintenance because an aircraft is only profitable when...

SCADA Data Mining for Condition Monitoring of a Wind Turbine Gearbox: A physics of failure and neural network based approach

Wind power is the fastest-growing energy source and the offshore wind industry is expected to grow by over 80 GW through 2024. There is a need to reduce all associated costs to be competitive in a market that might be fully subsidy-free soon. Adapting a predictive or condition-based maintenance strategy can help in reducing O&M costs, as it...

Predictive Maintenance for Aircraft Systems: Using textual elements as covariates

Unplanned maintenance is a costly factor in aircraft operations. Predictive Maintenance aims at reducing the surprise effect of unplanned maintenance and thereby its associated cost. A variety of statistical models are used to estimate the remaining life, as well as sensors to gauge component condition. The application to statistical models of...

Reliability modelling for Aircraft Component Availability Management

Global and local aviation traffic is growing while economic and performance pressures on the industry are increasing. As a consequence, airlines try to maximise their fleet utilization. Airline operators and Maintenance, Repair and Overhaul (MRO) providers therefore require as much insight as possible in factors affecting component reliability...

Remaining useful lifetime estimation on multi-modal failure in railway switches

In this day and age, failure of railway switches is managed using preventive and corrective maintenance, not predictive maintenance. These maintenance methods may allow for failure, where such a feailure could have been predicted. To combat this issue, this paper proposes a real-time predictive maintenance framework, which can identify the...

Gas Path Analysis as a Tool for the Predictive Maintenance of the Honeywell 331-500 Auxiliary Power Unit

This study examines Gas Path Analysis (GPA) as a method for the predictive maintenance of the Honeywell 331-500 Auxiliary Power Unit (APU). The 331-500 is used on the Boeing 777 and is susceptible to a sudden failure during operation due to the release of a first stage turbine blade. Investigations found the failure mechanisms of the blade...

Scalability Analysis of Predictive Maintenance Using Machine Learning in Oil Refineries

Modern refineries typically use a high number of sensors that generate an enormous amount of data about the condition of the plants. This generated data can be used to perform predictive maintenance, an approach to predict impending failures and mitigate downtime in refineries. This research analyzes the scalability of machine learning methods...

Machine Learning for Predictive Maintenance: A Boeing 747 Bleed Air Valves case study

The newest generation of aircraft has seen a strong increase in sensor data generated on-board. The available data has the potential to indicate the health state of individual components based on which their maintenance requirements can be determined, a maintenance strategy called Condition Based Maintenance. Predictive Maintenance is a specific...

Remaining Useful Life Estimation Using Explicit Duration Hidden Markov Models

Hidden Markov models are suited for modelling processes whose state is not<br/>directly visible, but the outputs of which depend on its state. As such they can be used to model degradation of machines. Hidden semi Markov models extend hidden Markov models by removing the inherent geometric constraint imposed on hidden Markov models.<br/>Using...

Predictive maintenance for aircraft components using proportional hazard models

Unscheduled maintenance can contribute significantly to an airline's cost outlay. Reliability analysis can help to identify and plan for maintenance events. Reliability analysis in industry is often limited to statistically based approaches that incorporate failure times as the primary stochastic variable, with additional strict assumptions...

The potential of advanced data analytics using machine learning to increase overall equipment effectiveness in an underground mining operation

Changing market dynamics make that mining companies need to increase their equipment utilization and use their equipment more effectively. The growing availability of data from mining equipment and mining operations enables the use of data analytics to achieve that goal. This thesis explores the potential of data analytics and machine learning...

Detecting central heating boiler malfunctions using smart-thermostat data

With the advent of smart thermostats like Toon®, detailed information about operation and usage of central heating boilers has become easily available. However, this information is not used in a systematic way by most companies including Eneco, and the wealth of information within is not available to the users. This has a few drawbacks. For...

CoBaPreM Component-Based Predictive Maintenance to reduce unscheduled occurrences: A new strategy to improve reliability practices

Costs associated to unscheduled and excessively prudent maintenance can contribute significantly to an airline's expenditure. Due to the complex nature of aviation related operations, reliability practices have been limited. In an attempt to improve component reliability, i.e. reduce maintenance-related costs, a new strategy was proposed which...

Prognostics on Aircraft Components in the Field of Predictive Maintenance

A case study on the Boeing 747 Integrated Drive Generator for predicting the remaining useful life (RUL) of the component. Real aircraft data is used with the neural network methodology for the prognostic model. Cost savings can be achieved by applying the model in aircraft maintenance.