This research develops an equipment failure prognostics model to predict the equipment’s chance of survival, using LAD. LAD benefits from not relying on any statistical theory, which enables it to overcome the problems concerning the statistical properties of the datasets. Its main advantage is its straightforward process and self-explanatory results. Herein, our main objective is to develop models to calculate equipment’s survival probability at a certain future moment, using LAD. We employ the LAD’s pattern generation procedure. Then, we introduce a guideline to employ generated patterns to estimate the equipment’s survival probability. The models are applied on a condition monitoring dataset. Performance analysis reveals that they provide comprehensible results that are greatly beneficial to maintenance practitioners. Results are compared with PHM’s results. The comparison reveals that the LAD models compare favorably to the PHM. Since they are at their beginning phase, some future directions are presented to improve their performances.