different degradation trend. This difference in the degradation trends can then be considered as an uncertainty in the degradation models derived from the data related to the accelerated life tests. Uncertainty in prediction: uncertainty is inherent to any prediction process. Any nominal and/or degradation model predictions are inaccurate which is impacted by several uncertainties such as uncertainty in the model parameters, the environmental conditions and the future mission profiles. The prediction uncertainty can be tackled by using Bayesian and online estimation and prediction tools (e.g. Particle Filters and Kalman filter etc.). Uncertainty in failure thresholds: the failure threshold is important in any fault detection and prediction methods. It determines the time at which the system fails and consequently the remaining useful life. In practice, the value of the failure threshold is not constant and can change in time. It can also change according to the nature of the system, operating conditions and in the environment which it evolves. All these parameters induce uncertainty which should be considered in the definition of the failure threshold. Examples of uncertainty quantification can be found in. Commercial hardware and software platforms For most PHM industrial applications, commercial off the shelf data acquisition hardware and sensors are normally the most practical and common. Example commercial vendors for data acquisition hardware include National Instruments and Advantech Webaccess; however, for certain applications, the hardware can be customized or ruggedized as needed. Common sensor types for PHM applications include accelerometers, temperature, pressure, measurements of rotational speed using encoders or tachometers, electrical measurements of voltage and current, acoustic emission, load cells for force measurements, and displacement or position measurements. There are numerous sensor vendors for those measurement types, with some having a specific product line that is more suited for condition monitoring and PHM applications. The data analysis algorithms and pattern recognition technology are now being offered in some commercial software platforms or as part of a packaged software solution. National Instruments currently has a trial version (with a commercial release in the upcoming year) of the Watchdog Agent prognostic toolkit, which is a collection of data-driven PHM algorithms that were developed by the Center for Intelligent Maintenance Systems. This collection of over 20 tools allows one to configure and customize the algorithms for signature extraction, anomaly detection, health assessment, failure diagnosis, and failure prediction for a given application as needed. Customized predictive monitoring commercial solutions using the Watchdog