Nowadays, modern rotating machinery industries such as automotive, aerospace, turbo machinery, chemical plants, and power generation stations are rapidly increasing in complexity and in their everyday operations, which demand their systems to operate at higher reliability, extreme safety, and with lower production and maintenance costs. Therefore, accurate fault diagnosis of machine failure is vital to the operation of the related industries. The majority of machine imperfections has been related to gearbox faults (e.g., gears, shafts and bearings), which are subject to damage modes such as fatigue, impacts, and overloading. Faults not detected in time can result in severe damage to machinery, catastrophic injuries, and substantial financial losses. On the other hand, if a fault is detected in its early stages, corrective and preventive action can be taken to avoid any significant machine failure. Vibration monitoring, a method that is widely used to determine the condition of various mechanical systems, will be applied in this work. In data acquisition, a transducer is attached to the structure under investigation and the vibration signal is recorded. This signal is then processed to extract representative features for fault detection. Signal processing techniques are therefore required to extract representative features to assess the health condition of gearbox components. However, in practice, the theoretical frequencies and characteristic features of gearbox faults may be modulated and masked by parasitical frequencies due to numerous noisy vibrations, as well as by the complexity of the transmission mechanics. To solve the related problems, the objective of this research work is to propose new signal processing technologies to evaluate gearbox health conditions. This work will focus on fixed-axis gearboxes, in which all gears are designed to rotate around their perspective fixed centers. Firstly, an enhanced morphological filtering (eM) technique is proposed to improve signal-to-noise ratio. Secondly, under controlled operating conditions, an integrated Hilbert Huang transform (iHT) method is suggested for bearing fault detection. Thirdly, a leakage-free resonance sparse decomposition (LRSD)-based technique is developed for advanced vibration signal analysis to eliminate random noise and to recognize characteristic features for bearing in gearboxes health conditions. The effectiveness of the proposed techniques is verified by a series of experimental tests corresponding to different bearing and gearbox conditions.