Recent Advances in Machine Learning with Applications to IoT

The High Stakes of IIoT

Major Use Case: Failure Prediction

What’s Driving IIoT

  • Decreased sensor cost: Cheaper sensors allows us to capture more data because more of our devices have these sensors. In the beginning, sensors were used for operation. Now, we can use them for prognostics and diagnostics.
  • Decreased transmission cost: We no longer need to download information manually. The most popular method, however, is cell data, of which the cost has dropped dramatically.
  • Decreased storage costs: We process around 1.2 billion data bits per day. Cloud storage is more efficient and allows companies to store that data for future insight.
  • New algorithms: We’re achieving human levels of detection with the latest algorithms. In 2015, for example, ImageNet algorithms reached human-level performance on image classification. These algorithms are similar to impactful monitoring of IIoT.
  • Decreased computing costs: We can run powerful algorithms more cheaply because of advances in computing technology.

What Is the Value of Prognostics?

Challenges of Implementation

  1. Connectivity — Cell connectivity can be spotty. In remote and rural environments, there are fewer cell connectivity chances. In these extreme environments, machine failure presents even more significant issues.
  2. Data transmission issues — Machines transmit data only when the machine itself is turned on. If the machine is off, you don’t know if the lack of data is because of a critical error or because of purposeful shutdown.
  3. Failure/Repair data is inaccurate and inconsistent — The most crucial source of data is based on humans. Data entry relies solely on human input, and that can’t cover the breadth needed. Also, asset hierarchies must be standardized across companies and equipment types.
  4. Failure causes are difficult to ascertain — Machine learning algorithms haven’t reached human levels of causation understanding. THey’re looking for patterns, some of which may be learned incorrectly.
  5. There are many types of failure — The top failure code in most industries only accounts for around 4% of failure types according to McElhinney. This is another area where the breadth of information can be challenging to overcome.
  6. High-value failures are rare — There’s just a massive data imbalance making it more difficult.
  7. Sensor limitations — Fixed sensor placement dictates what information is available. Some events happen too quickly for current sensor technology.
  8. Machine aging — within a fleet, you may have radically different ages of the same machine, making it difficult to create consistent rules and models.
  9. Differences between fleets — Even with controlling for differences in type or age, there could be variances in certain signals between fleets.
  10. Seasonality in the data — You must account for seasonality data in addition to fleetwide and age differences.
  11. Difficult to measure value — Operations are complex and nonlinear. For example, one broken-down train also causes delays in other, still-operational trains.
  12. Out of order events — Connectivity issues and latency sometimes deliver data out of order. For us, this is intuitive. Machines find this problematic.

The Main Approaches to Overcoming Obstacles for Recent Advances in Machine Learning and IIoT

Physics-based model

Data-Driven Failure Models

  • knowledge-based models — useful for inferences from previously observed situations
  • life expectancy models — manages life expectancy and degradation
  • artificial neural networks — computes predictions and estimations using observation data. Transfer learning falls into this area and has massive potential for prognostics.
  • physical models — computes predictions and estimations using the physical behavior of degradation

The Future of IIoT and Analytics



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