Tech is taking maintenance operations by storm.
The solutions used by maintenance departments are being thoroughly disrupted. The latest is a type of predictive technology known as Machine Learning.
Nowadays, Machine learning in maintenance growing rapidly.
With Machine Learning, the system is able to focus on the trends that matter. Here’s a definition by Azure Machine Learning:
Machine Learning examines large amounts of data looking for patterns, then generates code that lets you recognize those patterns in new data. Your applications can use this generated code to make better predictions. In other words, machine learning can help you create smarter applications.
Workers on the ground can’t always tell when it’s the right time to do maintenance.
Machine Learning takes data challenges into account that may otherwise go unnoticed. The following three enhancements use Machine Learning in maintenance, and are accessible from the palm of a maintenance technician’s hand:
1. Natural language processing
A big challenge for today’s maintenance technicians is that they have access to data on the go, but it’s difficult to handle on a smartphone or tablet.
Between thumbing through the text and organizing it all, there’s a lot of data within a Asset Management software. This is where natural language processing comes in.
The latest maintenance solutions are developing mobile applications with speech recognition. This means the technician can talk to a device to automate tasks while on the go.
Here’s a great example by Plant-Maintenance between a computer (C) and a technician (T):
C: Welcome back Marshall. Your choices are Work Orders, Equipment, Inventory, or More choices.
T: Work orders.
C: Update, Create, Work Order Details?
C: What is the Work Order number?
T: One zero zero one.
C: What would you like to update. Say “Choices” to hear a list of options.
T: Set failure class to pumps.
T: Set problem to low volume.
T: Set status to approved.
C: Are you done with this work order?
C: Anything else?
In this example the maintenance technician knows the commands needed to complete a work order and the solution is able to apply the technician’s updates through those verbal commands.
With recent enhancements to voice-activated applications, consumer apps have already incorporated this tech through Cortana and Siri. Operating a machine hands-free using voice recognition is revolutionary to the maintenance world.
2. Prescriptive logic trees
The purpose of every line of code is to give a “yes” or “no” command.
Machine Learning turns these commands into logic trees, which are familiar to the manufacturing field, but go an extra step when incorporating this technology.
Many factors come into play when making a decision. The tasks involved with working on a machine are influenced by many variables that a predictive maintenance solution can quickly consider. These variables could be:
- Age of the equipment
- Number of spare parts
- Failure rates
- Experience of the technician
- Environmental factors
Logic trees are able to break down the variables of a question to give a reliable answer. The technician can then take an action to fix the problem.
This is the core difference between predictive maintenance, which has recently become popular, and the rise of Machine Learning. Predictive technology completes the task once it pinpoints when the problem will occur. Machine Learning takes this a step further by suggesting a corrective action.
The solution can predict an outcome, prescribe a solution and then learn from the results when incorporating Machine Learning to be even more accurate next time.
This way, every problem and solution is properly balanced between risk and accuracy.
3. Cloud storage
Data storage has drastically changed. For both business and personal use, cloud storage has become the easiest and most efficient way of storing and accessing data.
The speed of cloud storage has creative a variety of software enhancements and application-based technology. Many cloud products have become mainstream for both consumers and enterprise users. GPS tracking and real-time communication through mobile devices are just two major enhancements.
CMMS are using the cloud in order to rapidly collect and apply insights from data. As mobile technology becomes more and more useful to technicians, the data those workers store while on the job can be used dynamically.
If the company has multiple enterprise solutions for storing cloud data, they can be integrated in order to carefully track the impact of all data.
That means insights can be shared across the company to bolster the bottom line and give a holistic overview to decision makers.
Author Bio: Julia Scavicchio is a writer with Better Buys, a trusted source on enterprise software news and research.