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The use of planetary gearboxes in heavy-duty industries is dominant due to their compact size, large transmission ratio and torque delivery capability with different configurations. Due to their harsh operating conditions, localised gear tooth faults such as cracking and chipping are more common in such gearboxes. Furthermore, localised gear tooth failure initiates distributed gear faults such as pitting and wear on the gear tooth. Therefore, it is necessary to monitor such localised gear faults continuously and detect them at an early stage to prevent sudden and catastrophic failure. In this study, gear tooth localised defects on various gear elements of the planetary gearbox are seeded using Electrical Discharge Machine (EDM). Then the vibration signals from the gearbox are captured. Afterwards, a decision tree algorithm selects the most prominent statistical features from many extracted features.

In the metal-cutting process, the condition of the cutting tool is critical. The tool condition is one of the factors that impact the surface finish. Monitoring the tool’s condition is necessary to ensure the quality of the end result and productivity. Because vibration signals have a strong relationship with tool state, vibration signals were captured in this investigation while milling mild steel specimens with carbide inserts in a vertical milling machine. Four tool conditions were considered in this study, namely, a good tool (G), a tool with nominal flank wear (FW), tool flaking on the rake face (FL), and tool breakage (B). Histogram features were extracted from the captured vibration signal. J48 algorithm is used to select relevant features, which are then fed into Support Vector Machine (SVM) and K-Nearest neighbourhood (KNN) algorithms. SVM and KNN classification abilities are compared.

Gears are important machine elements that transfer motion by the meshing of teeth. In this modern era gearbox has become an essential component in industry as well common man day to day life. In most of the industrial conditions gear boxes are subjected to continuous operation, which in many cases evades the maintenance activities. In such scenario the gear box may experience an unexpected failure, which will lead to shut down of the specific unit. Considering the significance of the gearbox, condition monitoring of gear box becomes essential. The helical gear box consists vital components like, helical gears of different ratios, bearing, shaft, gear shifting rod, plumber block etc. But the component which is prone to frequent failure must be prioritized and condition monitored, to ensure a continuous operation of the gearbox. That gives a scope for classification problem using machine learning algorithms.

Epicyclic geartrains are often preferred in heavy-duty machinery owing to their abilities such as transmitting large amounts of power with minimal loss, good load sharing capacity, large reduction ratios, and compact design. Machinery employing such complex geartrains need an effective monitoring system to predict gear failure at an early stage which prevents catastrophic failure. In this work, vibration signal of the geartrain is acquired using an accelerometer under various gear fault conditions such as healthy gear, defect in sun gear, defect in planet gear, defect in ring gear, defect in both sun and planet gears respectively. Then, statistical characteristics or features such as mean, median, mode, variance, skewness, kurtosis, standard error, standard deviation, maximum and minimum, of the time domain vibration signals are extracted. Afterward, a decision tree algorithm is used to select the most useful statistical features.

Drive train failures are most common in wind turbines. Lots of effort has been made to improve the reliability of the gearbox but the truth is that these efforts do not provide a lifetime solution. Majority of failures are caused by bearing and gearbox. It also states that wind turbine gearbox failure causes the highest downtime as the repair has to be done at Original Equipment Manufacturer [OEM]. This work aims to predict the failures in planetary gearbox using fault diagnosis technique and machine learning algorithms. In the proposed method the failing parts of the planetary gearbox are monitored with the help of accelerometer sensor mounted on the planetary gearbox casing which will record the vibrations. A prototype has been fabricated as a miniature of single stage planetary gearbox. The vibrations of the healthy gearbox, sun defect, planet defect and ring defect under loaded conditions are obtained. The signals show the performance characteristics of the gearbox condition.

In this work an attempt is made to design and fabricate a low cost dynamometer for measuring cutting forces in three directions in a CNC vertical milling machine. The dynamometer is designed and fabricated to withstand load up to 5000 N along ‘X’, ‘Y’ and ‘Z’ axis. Milling dynamometer developed in this work, consists of four octagonal rings as an elastic member on which strain gauges are mounted for measuring the cutting forces. Suitable materials for the fixture and for the octagonal rings are chosen for constructing the dynamometer. Structural analysis has been carried out to check the safe design of the dynamometer assembly consisting of fixture and the octagonal rings for the maximum loading conditions. Static calibration of the dynamometer is carried out using slotted weight method by simulating the actual conditions. Calibration chart was prepared for three directions by relating load and corresponding strain.