This paper presents a new approach, variant of the Direct Load Recognition (DLR) methodology, to estimate the main rotor (MR) pitch-link load on customer flights. The original DLR methodology is based on the combination of a harmonic decomposition and the use of Machine Learning algorithms. The DLR variant replaces the harmonic decomposition by a wavelet decomposition. The application of this paper consists in two parts. First, the comparison between the original DLR and DLR variant on prototype flight test data. Two results are highlighted in this part. The capacity of representation of the pitch-link load is better for the wavelet decomposition. The modelling of its coefficients enables to slightly improve the pitch-link load estimation, especially on the high load values having more impact on the fatigue computation. This first part allows to study the feasibility of the DLR variant to estimate the pitch-link load. The second part of this paper focuses on the application of the pitch-link load estimator built with DLR variant on the H175 fleet. From the estimated MR pitch-link load, the MR pitch horn damage is derived and compared to the Design Usage Spectrum (DUS), used today for the certification. The damage of all the studied customer aircraft is well below the DUS, showing a potential gain in component life time. The results of this paper manifest the advantage of the DLR methodology, and more precisely DLR variant methodology, for predictive maintenance on the MR pitch horn, that is to adapt the maintenance to the aircraft usage.