Profile monitoring is used to check the stability of the quality of a product over time when the product quality is best represented by a function at each time point.However,most previous monitoring approaches have not considered that the argument values may vary from profile to profile,which is common in practice.A novel nonparametric control scheme based on profile error is proposed for monitoring nonlinear profiles with varied argument values.The proposed scheme uses the metrics of profile error as the statistics to construct the control charts.More details about the design of this nonparametric scheme are also discussed.The monitoring performance of the combined control scheme is compared with that of alternative nonparametric methods via simulation.Simulation studies show that the combined scheme is effective in detecting parameter error and is sensitive to small shifts in the process.In addition,due to the properties of the charting statistics,the out-of-control signal can provide diagnostic information for the users.Finally,the implementation steps of the proposed monitoring scheme are given and applied for monitoring the blade manufacturing process.With the application in blade manufacturing of aircraft engines,the proposed nonparametric control scheme is effective,interpretable,and easy to apply. Profile monitoring is used to check the stability of the quality of a product over time when the product quality is best represented by a function at each time point. Home, most previous monitoring approaches have not considered that the argument values ​​vary vary profile to profile , which is common in practice. A novel nonparametric control scheme based on profile error is proposed for monitoring nonlinear profiles with varied argument values. The proposed scheme uses the metrics of profile error as the statistics to construct the control charts. More details about the design of this nonparametric scheme are also discussed. the monitoring performance of the combined control scheme is compared with that of an alternative nonparametric methods via simulation. Simulation studies show that the combined scheme is effective in detecting parameter error and is sensitive to small shifts in the process. In addition, due to the properties of the charting statistics, the out-of-control signal can provid e diagnostic information for the users .Finally, the implementation steps of the proposed monitoring scheme are given and applied for monitoring the blade manufacturing process.With the application in blade manufacturing of aircraft engines, the proposed nonparametric control scheme is effective, interpretable, and easy to apply.