  Accurate aircraft-mass estimation is critical to airlines from the
safety-management and performance-optimization viewpoints. Overloading an
aircraft with passengers and baggage might result in a safety hazard. In
contrast, not fully utilizing an aircraft's payload-carrying capacity
undermines its operational efficiency and airline profitability. However,
accurate determination of the aircraft mass for each operating flight is not
feasible because it is impractical to weigh each aircraft component, including
the payload. The existing methods for aircraft-mass estimation are dependent on
the aircraft- and engine-performance parameters, which are usually considered
proprietary information. Moreover, the values of these parameters vary under
different operating conditions while those of others might be subject to large
estimation errors. This paper presents a data-driven method involving use of
the quick access recorder (QAR)-a digital flight-data recorder-installed on all
aircrafts to record the initial aircraft climb mass during each flight. The
method requires users to select appropriate parameters among several thousand
others recorded by the QAR using physical models. The selected data are
subsequently processed and provided as input to a multilayer perceptron neural
network for building the model for initial-climb aircraft-mass prediction.
Thus, the proposed method offers the advantages of both the model-based and
data-driven approaches for aircraft-mass estimation. Because this method does
not explicitly rely on any aircraft or engine parameter, it is universally
applicable to all aircraft types. In this study, the proposed method was
applied to a set of Boeing 777-300ER aircrafts, the results of which
demonstrated reasonable accuracy. Airlines can use this tool to better utilize
aircraft's payload.
