With respect to future trends in cabin design, this paper takes a close look at two problem areas that produce delays and reduce the time an aircraft is in the air. The first is related to unscheduled maintenance and  the second is related to necessary cabin reconfigurations. Each of the two problem areas are analyzed for time consumption and costs. The research project PAHMIR (Preventive Aircraft Health Monitoring for Integrated Reconfiguration, a cooperation project between Airbus Germany and Hamburg University of Applied Sciences) ads “intelligence” to aircraft components and novel technologies to the aircraft system. These measures will be able to reduce time consumption and costs for unscheduled maintenance caused by the air conditioning system in the order of 8 %. These measures will be able to reduce time consumption for cabin reconfiguration by about 36+%. The upper price limit for economic intelligent quick fasteners seems to be around 500 USD.

Complex systems are commonly monitored by many sensors. When one of the sensors fails, the current state of the system can not be calculated or the information about the current state is not complete. For that reason sensor failures are one of the main error sources of a system. Thus sensors that deliver significant information about the system state need to be redundant. This paper shows how to calculate the significance of the information that a sensor gives about a system by using modern signal processing and artificial intelligence technologies. It also shows how significant features can be extracted, evaluated from a set of data samples, how difficult it is to find an optimal parameter and sensor set and that it is possible to reduce the size of needed data by 97%. The paper concludes analyzing the results of experiments that show that the methods can classify different errors with a 75% probability.

In the PAHMIR (Preventive Aircraft and Health Monitoring) project pattern recognition and signal analysis is used to support and simplify the monitoring of complex aircraft systems. The parameters of the signal analysis need to be chosen specifically for the monitored system to get the best pattern recognition accuracy. An optimization process was developed that uses global heuristic search and optimization to find a good parameter set for the signal analysis. The computed parameters deliver slightly (one to three percent) better results than the ones found by hand. In addition it is shown that not a full set of data samples is needed. Genetic optimization showed the best performance.

A reliable condition monitoring is needed to predict faults. Pattern recognition technologies are often used for finding patterns in complex systems. Condition monitoring can also benefit from pattern recognition. Many pattern recognition technologies, however, only output the classification of the data sample but do not output any information about classes that are also very similar to the input vector. This paper presents a concept for pattern recognition that output similarity values for decision trees. The concept can be used on top of any normal decision tree algorithms and is independent of the learning algorithm. Performed experiments showed that the concept is reliable and it also works with decision tree forests to increase the classification accuracy.

Unscheduled maintenance of aircraft can cause significant costs. The machine needs to be repaired before it can operate again. Thus it is desirable to have concepts and methods to prevent unscheduled maintenance. This paper proposes a method for forecasting the condition of aircraft air conditioning system based on observed past data. Forecasting is done in a point by point way, by iterating the algorithm. The proposed method uses decision trees to find and learn patterns in past data and use these patterns to select the best forecasting method to forecast future data points. Forecasting a data point is based on selecting the best applicable approximation method. The selection is done by calculating different features/attributes of the time series and then evaluating the decision tree. A genetic algorithm is used to find the best feature set for the given problem to increase the forecasting performance. The experiments show a good forecasting ability even when the function is disturbed by noise.