Failure prediction based on sensor data of pump stations for Aquafin @ Asset Performance 4.0

<p><strong>DEADLINE CHANGE</strong></p><p>On general request, we've decided to make a change:</p><p><strong>The deadline is extended to JUNE 15th.&nbsp;</strong></p><p>Companies who send in their presentations before June 1st, will DEFINITELY be featured during the Asset Performance 4.0 Conference. Their <strong>speaking slot is guaranteed</strong>.</p><p>&nbsp;</p><p>But: if you need more time to work on the perfect solution, you still can! We expect the last presentations before JUNE 15th. All presentations entered <strong>between June 1st and June 15th are still in the running</strong>, but might not be featured during the conference.</p><p>&nbsp;</p><p><strong>About the challenge</strong></p><p>Aquafin is responsible for the transport and treatment of household wastewater in Flanders. For that transport, it manages 1700 pumping stations and 318 water treatment plants. Around 40 teams spread across Flanders can be reached 24/7 in the event of a pump blockage or other malfunctions.&nbsp;</p><p>When a pumping station is not operational for too long, this leads to a discharge of untreated wastewater into our watercourses, which we naturally want to avoid. On the other hand, we want our operators to stop pumping at night as little as possible because this disrupts the team's planning.&nbsp;</p><p>Aquafin wants to be able to schedule urgent maintenance better during working hours and thus generally have smarter planning and more efficient operations with a better work/life balance as a result.</p><p>Aquafin wishes to investigate whether blockages or other disruptions can be&nbsp;predicted with sufficient reliability using intelligent data analysis techniques.</p><p>&nbsp;</p><p><strong>Prerequisites</strong></p><p>Aquafin is currently in the process of rolling out a new supervision system that captures the data of all installations (in addition to the pumping stations also 300 water treatment plants) and with which all installations can be operated remotely. In the meantime, approximately ¼ of our plants are linked to this central system.</p><p>&nbsp;</p><p><strong>Provided input</strong></p><p>A large number of measurements such as in/out of operation of the pumps, current, level in the pump well, flow, malfunctions and reports are historically available over a long period of time.</p><p>&nbsp;</p><p><strong>Target audience</strong></p><p>Data scientists, A.I. Machine Learning, asset management specialists.</p><p>&nbsp;</p><p>READ THE FULL CHALLENGE IN THE INSIGHTS.</p><p>&nbsp;</p><p><strong>TIMING</strong></p><p><strong>Start: </strong>31 March</p><p><strong>Submit your ideas before: </strong>15 June (Advantage if you submit before 1 June)</p><p><strong>The jury will select the finalists: </strong>30 June</p><p>&nbsp;</p><p>You will then have all summer to prepare your final presentations. You'll present them live or virtually on the<strong> Asset Performance 4.0 Conference in Ghent, Belgium.</strong></p>