Reducing Costs with Predictive Maintenance

The Nordic blog has an informative post on How IoT-Based Predictive Maintenance Can Reduce Costs. It explains how connected sensors can save maintenance costs through reduced downtime. The post provides some examples from the power industry and explains how the same techniques can be used in the tools, retail, distribution and physical infrastructure industries.

As the post mentions, the challenge is how to scale this up. We are told IoT is the solution. Here at BeaconZone, we don’t believe IoT is always the solution, especially where there’s a requirement for higher sensor sampling frequencies. There’s too much data, too much data transfer and too much server processing. It really doesn’t scale. Apart from the waste and cost of these resources, the latency of triggering events based on the data is too high. Instead, look to so called ‘edge’ or ‘fog’ computing where more processing is done nearer the sensors and only pertinent data is sent to other systems.

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Bluetooth Sensor Beacons For Prognostics

If you are considering using Bluetooth sensor beacons for prognostics, you should take a look at the free IEEE paper IoT-Based Prognostics and Systems Health Management for Industrial Applications (pdf).

Prognostics is the determination of health of assets to diagnose anomalous behaviour and predict the remaining useful life. It’s used to:

  • Prevent catastrophic failures
  • Increase asset availability through less downtime and less time wasted through ‘no fault found’ tests
  • Extend maintenance cycles
  • Execute timely repair

The overall aim is to lower lifecycle costs via fewer inspections, repairs and manual inspections. It can be applied to all types of assets across all sectors but is particularly applicable to manufacturing, industry and infrastructure. Infrastructure includes roads and ports as well as utility industries such as water, power and gas.

Prognostics and in-situ testing isn’t new. However, what is new is substantially improved viability and economics. New sensors, such as beacons, are easier to use, can be attached to legacy equipment and have much lower costs. The cost of connectivity and cloud storage is also decreasing. This means more assets can be retro-actively connected and the sharing of data across assets and platforms enables a more complete operating picture. This opens up new business opportunities.

The paper explains the four main types of prognostic management strategies: corrective, fixed-interval preventative, failure-finding, and condition-based maintenance (CBM). It also explains a new fusion approach to prognostics:

The paper gives examples of use of prognostics in the manufacturing, heavy industry, energy generation, transport & logistics, infrastructure assets, automobile, medical, warranty and robotic industries.

It ends with the mention that, in the future, current research and work on energy harvesting will benefit sensors used for prognostics.

More information:

Sensor Beacons
Beacon Proximity and Sensing for the Internet of Things (IoT)
Beacons in Industry and the 4th Industrial Revolution (4IR)
Using Bluetooth Wireless Sensors

Prognostics, Predictive Maintainance Using Sensor Beacons

A growing use of sensor beacons is in prognostics. Prognostics replaces human inspection with continuously automated monitoring. This cuts costs and potentially detects when things are about to fail rather than when they have failed. This makes processes proactive rather than reactive thus providing for smoother process planning and reducing the knock-on affects of failures. It can also reduce the need for over excessive and costly component replacement that’s sometimes used to reduce in-process failure.

Prognostics is implemented by examining the time series data from sensors, such as those monitoring temperature or vibration, in order to detect anomalies and make forecasts on the remaining useful life of components. The problems with analysing such data values are that they are usually complex and noisy.

Machine learning’s capacity to analyse very large amounts of high dimensional data can take prognostics to a new level. In some circumstances, adding in additional data such as audio and image data can enhance the capabilities and provide for continuously self-learning systems.

A downside of using machine learning is that it requires lots of data. This usually requires a gateway, smartphone, tablet or IoT Edge device to collect initial data. Once the data has been obtained, it need to be categorised, filtered and converted into a form suitable for machine learning. The machine learning results in a ‘model’ that can be used in production systems to provide for classification and prediction.