Malfunctions on industrial robots can cost factories 22 000 dollars per minute. Although the benefits of a fault-tolerant robot arm are clear, redundant sensors would steeply add to the costs of such robots while machine learning-based methods would spend too much time learning the robot's model. We propose a simple but highly effective method to infer which joint underwent failure and at which angle this joint is constrained and to, then, modify the inverse kinematics (IK) algorithm to adaptively achieve the goal. Our method involves combining the robot arm with a QR code and an inexpensive camera, building a virtual link between these three to give the relative position of the end-effector. Once one joint/encoder/motor suffers damage, we use this virtual link to calibrate this joint by coordinate transformation, to calculate the constrained angle, and to recalculate the trajectory through IK iterations with the Newton-Raphson method. We prove the efficacy of our method with pick-and-place experiments, commonly seen in industrial settings, emulating malfunctions on different joints and at different angles, and our method can successfully finish the task in most cases. We further demonstrate that our method is capable, for almost all of the six-degree-offreedom manipulators, to adapt to joint failures after suffering an actuator failure. With the steep increase of robots within factories, this paper presents an elegant approach to keep robots functional until maintenance is scheduled, reducing downtime.