Unmanned Aerial Vehicles (UAVs) are quite successful in maintaining steady flight operations, but propeller failure that exists causes them to experience a possible crash. The objective of this thesis project is to propose a UAV failure detection model as part of the developing framework of an autonomous emergency landing system for UAVs. Health monitoring is integrated where the quadcopter is flown for three cases of propeller faults. Vibration signals are measured during each flight, where a hardware system is designed with Arduino Uno and an Inertial Measurement Unit (IMU) sensor that contains a 3-axis accelerometer and a 3-axis gyroscope, and vibration graphs are made. Once the data is extracted, different parameters (aX, aY, aZ, gX, gY, and gZ) are selected with dimension n ∈ {1,2,3,4,5,6}, and 750 data points are chosen for the K-Means Clustering algorithm. Quadcopter Failure Detection Cluster (QFDC) plots and confusion matrices are created, and three different health states are classified as clusters – normal state, faulty state, and failure state. The parameter set gZ-aZ has the best performance metrics with an accuracy of 92.1%, which is chosen for the decision-making step that involves a Light Emitting Diode (LED) subsystem. Boundary conditions are set from the gZ-aZ QFDC plot where three LEDs turn on based on the specified health state to validate the model. The accuracies of the LED system range between 89% and 95%. Successful failure detection for UAVs would make UAVs safer and more reliable to fly with less imposed restrictions.