We recommend you cite the published version. The publisher's URL is: http://dx.doi.org/10.1007/s10846-013-0020-7 Refereed: YesThe final publication is available at Springer via http://dx.doi.org/10.1007/s10846?013?0020?7 Disclaimer UWE has obtained warranties from all depositors as to their title in the material deposited and as to their right to deposit such material. UWE makes no representation or warranties of commercial utility, title, or fitness for a particular purpose or any other warranty, express or implied in respect of any material deposited. UWE makes no representation that the use of the materials will not infringe any patent, copyright, trademark or other property or proprietary rights. UWE accepts no liability for any infringement of intellectual property rights in any material deposited but will remove such material from public view pending investigation in the event of an allegation of any such infringement. PLEASE SCROLL DOWN FOR TEXT.Metadata of the article that will be visualized in OnlineFirst As autonomous mobile robots become a commer-23 cial reality, attention must be paid to the problem 24 of assuring their safety. In almost every application 25 of mobile robots other than toys, the size, power or 26 speed of robots will be such that potential hazards 27 will be associated with their operation or malfunction. 28 Legal regulations in most countries require that any 29 such safety critical system be designed so as to reduce 30 the risk of accidents caused by these hazards to less 31 than some required threshold, or at least as low as is 32 reasonably practicable. 33The achievement of safety in engineering systems 34 requires a combination of different approaches of 35 safety requirements specification, analysis, design and 36 manufacturing inspections, and product testing. for future work that emerge from these studies.

Recent development trends in wind power generation have increased the importance of the safe operation of wind-turbine blades (WTBs). To realize this objective, it is essential to inspect WTBs for any defects before they are placed into operation. However, conventional methods of fault inspection in WTBs can be rather difficult to implement, since complex curvatures that characterize the WTB structures must ensure accurate and reliable inspection. Moreover, it is considered useful if inspection results can be objectively and consistently classified and analyzed by an automated system and not by the subjective judgment of an inspector. To address this concern, the construction of a pressure-and shape-adaptive phased-array ultrasonic testing platform, which is controlled by a nanoengine operation system to inspect WTBs for internal defects, has been presented in this paper. An automatic classifier has been designed to detect discontinuities in WTBs by using an A-scanimaging-based convolutional neural network (CNN). The proposed CNN classifier design demonstrates a classification accuracy of nearly 99%. Results of the study demonstrate that the proposed CNN classifier is capable of automatically classifying the discontinuities of WTB with high accuracy, all of which could be considered as defect candidates.