Failures of automotive mechanical systems such as suspension systems, or “springs” while a vehicle is in operation is most often serious, and can sometimes incur financial loses or even fatal consequences. Spring failures result from either chronic overloading, poor driver behavior, severe duty cycle, or a combination of the aforementioned conditions. These conditions result in extraordinary fatigue, ultimately reducing the spring’s overall expected useful life. There has been a significant reduction in the cost of onboard computing making it economically viable to measure, record, and store various parameters that affect the spring life. An economical measurement system was designed that could plot the Load vs. Displacement graph (L/D) by simply measuring the spring’s displacement from its nominal (static) position. Previous methodologies have used expensive load cells; in our demonstration we prove that measurements can be taken much more economically using an array of Hall Effect type sensors. The L/D plots are stored and compared and a performance deterioration curve is plotted and an acute failure timeline is predicted, information of which is constantly relayed to the cloud. Alarm flags are raised at the manufacturer’s/supplier’s front-end by an intuitive app and further actions may be planned. An onboard display can be used to inform driver about its haul-mass, optimum speed bracket to be maintained and even inform about regular checkup deadlines. Hence, when implemented, the technology can be useful for failure prognosis by OEMs, tier ones, service agencies and Insurance Agencies.