Spatial Digitalisation of Inspection and Test Data at Applus+ in Australia

11/10/2021

    As the volume of inspection and test data continues to expand and the digitalisation of our inspection and testing processes gathers pace. Many of our clients are continuing to invest heavily in Artificial Intelligence that provides predictive maintenance solutions.

    The predictive algorithms that our clients create are heavily dependent on the depth, accuracy, and timeliness of the data we provide. One of the essential requirements for these algorithms is that inspection and test data is accessible and in a format they can digest. Specifically, the spatial location of asset anomalies is crucial. The conventional method of recording anomalies on 2D drawings or describing the location as a narrative is time-consuming and lacks accuracy.
     
    This method leaves little ability for the practical analysis of the data in a time-effective manner. Applus+ have been generating reality models for several years. Reality models are photo-realistic 3D representations of an asset; they provide accurate spatial context for data. Previously the processing of these models and the association of data was time-consuming. However, they can now be rapidly generated, and we provide an online platform for applying and reviewing inspection and test data.
     
    By applying our data to these models, we can then provide our clients with the required spatial information. Not only is this information important for predictive models, but it also aids the interpretation of our results when viewed with spatial context. Greater appreciation and awareness of digitalisation enable managers, inspectors, and technicians to incorporate digital tools into our services.; Continuing to aid our clients in keeping their facilities safe and efficient.

    Applus+ uses first-party and third-party cookies for analytical purposes and to show you personalized advertising based on a profile drawn up based on your browsing habits (eg. visited websites). You can accept all cookies by pressing the "Accept" button or configure or reject their use. Consult our Cookies Policy for more information.

    Cookie settings panel