Asset Management

Can the Internet of Things save New Orleans from flooding?

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Following weeks of uninterrupted rainfall, a five-hour downpour on top of already saturated streets led to extensive flooding in New Orleans last Saturday.

While it is widely held that a weather event of this magnitude would have overwhelmed even a pump system in perfect working order, the city’s aged and out-of-action pumps and turbine generators undoubtedly contributed to the problem, and heads have rolled for it at the Sewage and Water Board.

As hurricane season approaches, the city will face some tough questions. Could New Orleans withstand flooding of this magnitude during a hurricane? How can we solve the problem of aging infrastructure, and what are the next steps?

The answer is not a simple one. While an ideal scenario would naturally be to replace the venerable pump system with something up-to-date, this is a daunting task requiring a level of investment that simply isn’t available.

However, instrumenting existing assets with connected sensors could help us listen to this equipment better, reducing downtime and helping to plan maintenance more effectively. An Enterprise Asset Management (or EAM) solution like IBM Maximo could go some way to giving better visibility into the health of the city’s drainage assets: both the pumps themselves and the turbines the power them.

New Orleans: The underwater city

To understand the situation in which New Orleans finds itself, we need to take a closer look at the city’s topography. Critically, the city is below sea level, meaning that it can’t rely on gravity to drain excess water from the streets. Instead, New Orleans relies instead on a system of 120 pumps – some as big as a car garage – to drain and redistribute excess water.

The problem: Aging and faulty infrastructure, and the preventive maintenance method

Unfortunately, the system is – well – old. Some of the pumps have 100 years to their credit, while several of the five turbines that power them predate the second world war. The age of these assets means that they are liable to experience faults: over the weekend when the worst of the flooding took place, 16 of the pumps were out of action, and three of the five turbines had been offline for weeks, leaving a skeleton service in place to deal with the deluge.

It seems that the condition of the pumps is monitored and managed using a traditional preventive maintenance model, which relies on regular, scheduled inspections to maintain asset health. The problem with a system like this is that sometimes assets are subjected to maintenance that they don’t need, to fall in with the prearranged schedule, as it is difficult to anticipate exactly when they will need care.

According to a recent report on plant maintenance, 30% of preventive maintenance activities are carried out too frequently, and 45% of these efforts are ineffective. Worse still, they may lead to asset failure, by disrupting already stable assets with unnecessary works.

By contrast, a predictive maintenance system, which uses sensor data to constantly monitor the way an asset responds to regular use, can eliminate breakdowns by up to 70%, reduce downtime by up to 50%, and reduce scheduled repairs by up to 12%. An Enterprise Asset Management system such as IBM Maximo uses this predictive model to ensure that the need for asset repair can be anticipated in advance, allowing work to be scheduled only when it becomes necessary.

How does an Enterprise Asset Management system work?

Instrumenting assets (in this case, pumps) with IoT sensors means that they can tell us when they need fixing. Data from these sensors is input into an Enterprise Asset Management system, like IBM Maximo, which analyzes for patterns and identifies the state of the asset’s health, using a real-time health scoring tool like Maximo Asset Health Insights (MAHI).

Rather than relying on standard recommendations from a manufacturer, operations managers know in real-time how the asset is performing and if there are problem areas. They know if maintenance is not needed for nine months even though the recommendation is for six. This leads to longer life spans of assets because they are only fixed when they need to be. It also saves on company resources and budget for new equipment can be used where it’s really needed.

Furthermore, Maximo gives visibility into location and information about all assets connected to the network. The result is a connected, visible operation, allowing for a proactive approach to maintenance and upkeep.

Learn more

Visit our website to discover how IBM Maximo uses the power of IoT data to bring together people, assets and organizations, for more streamlined operations and enhanced enterprise performance.

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