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Rolling element bearing faults of a laboratory scale wind turbine gearbox operating under nonstationary loads have been diagnosed using condition monitoring (CM) techniques such as vibration analysis, acoustic analysis, and lubrication oil analysis. Two local bearing faults, namely, bearing inner race fault and bearing outer fault are seeded in the gearbox. The raw data from these techniques are decomposed and wavelet approximation coefficients of level four (a4) are extracted using discrete wavelet transform (DWT). A plethora of statistical features is computed from the wavelet approximation coefficients and the most significant features are being identified by implementing the decision tree algorithm. The classification efficiencies of each of these CM techniques are compared by using the support-vector machine algorithm. Furthermore, an integrated CM scheme is developed by combining the individual CM techniques and the fault diagnosing ability of the integrated CM scheme is compared with the individual CM techniques. A principal component analysis-based approach is used as a feature classification algorithm and an input feature matrix is formed by combining the significant features extracted from vibration, acoustic, and lubrication oil analysis. It has been observed that the integrated CM scheme has provided better classification interpretations than the single CM techniques and it can be extended for real time fault diagnosis of a wind turbine gearbox.

The addition of carbon nanofibers (CNFs) and graphene to thermoplastic polymers has the potential to increase the mechanical and electrical properties. The aim of the current study is to investigate the effect of CNF and graphene on the properties of three‐dimensional (3D) printed polylactic acid (PLA) samples. The samples are printed using the PLA–graphene–CNF composite filament via fused deposition modeling. As a first objective, composite filament and pure PLA filament characteristics were compared using tensile, scanning electron microscopy and thermogravimetric analysis (TGA) data. Compared with pure PLA filaments, composite filaments revealed an almost 28% reduction in tensile strength due to interfacial defects (poor bonding between PLA and graphene and CNF). Furthermore, 3D printed composite sample properties are compared with 3D printed pure PLA sample using tensile and electrical conductivity data. The 3D printed composite samples also showed less tensile strength (25 MPa) compared with pure PLA 3D samples (37–47 MPa) due to the presence of microvoids. These voids also caused reduction in tensile strength and electrical conductivity of 3D printed composites when compared with the filament properties. The effect of layer orientation was also studied by printing samples at [0°/90°] and [45°/−45°] orientations and found that samples with [45°/−45°] orientation revealed higher elongation (>100%) and lower electrical conductivity. It is observed that the presence of graphene and lack of bonding between PLA and CNF resulted in the improper fusion of the printed layers, which led to a decrease in tensile strength and electrical conductivity but an increase in toughness.