Failure Prediction Machine Learning: Using Machine Learning to Find Failures Before They Occur

Failure prediction machine learning is the application of artificial intelligence within the maintenance arena. Effective, affordable technology is now available to help you monitor your critical assets around the clock. As soon as monitoring equipment detects something out of the ordinary, it can automatically communicate with a centralized computer system. This allows you to take action before, or immediately after, failures happen.

Predictive, Preventive, and Reactive Maintenance

Most companies operate somewhere on the spectrum between 100 percent reactive maintenance and 100 percent predictive maintenance. Typically, facilities have elements of both reactive and predictive maintenance, as well as some preventive maintenance strategy too. It’s important to emphasize that the closer a business can move toward predictive maintenance, the better. Using machine learning effectively will help move your organization in that direction, which leads to better performance and lower costs.

Why Machine Learning Beats Human Monitoring

In this age of artificial intelligence, it’s important to understand what machines can do better than humans and what humans can do better than machines. Both are critical to maintaining a successful company if strengths are understood and employed. Machine monitoring can be done constantly; human technicians can only monitor assets periodically.

Real Time Alerts

Since equipment such as sensors can monitor constantly, they can provide real-time alerts as soon as a potential problem arises. For example, technicians can specify acceptable ranges. Machines can send an alert as soon as an asset falls out of that range. If a person manually checks ranges, even daily, you could have a 24-hour lag time before the problem is detected.

Predictive Repairs

At a more advanced level, machine learning can use past data to generate conclusions. For example, it may be able to calculate the likelihood that errors will result in a full malfunction or failure. Management can then use this analysis to perform predictive maintenance tasks before the failure occurs.

Malfunction Sensing

In some cases, it may be safer and less damaging if a process or machine stops before a malfunction occurs. One common application is that many machines will cut the power before an engine overheats, causing much more damage. You may program specific assets to pull the plug, as soon as a malfunction happens.

Less Data Sifting

Machines can sift through thousands of data points every second, making conclusions based on human direction and programming. Humans simply cannot process data in the same manner or speed.

Six Ways Machine Learning Improves Systems

Machine learning can help just about any organization lower costs by facilitating faster repairs. This leads to less downtime overall and makes scaling up as demand increases easier. Most importantly, machine learning can contribute to a safer work environment for all.

1. Lower Costs

Employing sensors and artificial intelligence can be much less expensive than relying on engineers or technicians to perform the same tasks.

2. Quicker Repairs

Because machine learning provides around-the-clock monitoring, you’ll be alerted to needed repairs immediately. Trained maintenance technicians can be sent to address the potential problem or malfunction right away, keeping things running.

3. Safer Systems

Sensors and machine learning can spot problems early and be programmed to shut down before something catastrophic happens. This increases safety for all your employees, especially when working around potentially dangerous processes or equipment.

4. Less Downtime

The fact that sensors and machine learning can let you know when potential problems might occur leads to less downtime. Significant failures can easily halt a production line, possibly for hours at a time. If you discover your line is at risk due to a machine learning alert, you can order needed parts and schedule predictive maintenance tasks at convenient times.

5. Easier Scalability

Machine learning leads to everything running more smoothly. As your company grows, you’ll be able to scale production and expansion more easily and effectively. If you approach a maximum capacity, your machine learning will be able to warn you before you overload your system.

6. Higher Level Work for Employees

Although there is a fear of machines taking human jobs, remember that machines are best at tedious tasks. This should free up technicians and engineers to do more challenging and rewarding tasks. Be sure to invest some of your AI cost savings in helping your employees grow in other knowledge and skills.

How Machine Learning Works in Predictive Maintenance

Machine learning takes a methodical approach to contribute to a predictive maintenance program. Initially, data is collected from sensors and when performance falls out of range, alerts are sent. Longer term, however, machine learning can process ongoing performance and provide data about frequency of threats and potential consequences.

1. Collect Data from Sensors

Use a wide variety of sensors on your critical equipment to collect data. Sensors can monitor temperature, vibration, water leakage or levels, mileage or usage data, pressure, and many other performance items.

2. Extract Stand-Out Anomalies

Specify within your system the acceptable ranges of the factors you’re monitoring. If anomalies arise, program your machine to send necessarily alerts. Particular anomalies may include pressure peaks, temperature peaks, or vibration peaks.

3. Analyze Anomalies to Detect Threats

The machine learning tools can be programmed to analyze potential threats. When a certain level is reached or exceeded, an immediate alert can be sent. However, machine learning can also look at the history of anomalies for a particular asset to determine larger failure threats.

4. Report Threats in CMMS

Once threats are detected, sensors and machine learning equipment can send the data to your computerized maintenance management system (CMMS). At that point, your CMMS can initiate purchase orders if parts are needed or work orders to address the threat.

5. Machine System Learns

After you’ve generated some historic data, your machine learning system can consider these data points to draw some conclusions. For example, it can suggest potential threats or report on the frequency of anomalies on a particular piece of equipment. Using this data, you can make more methodical business decisions.

Conclusion

Machine learning is an excellent way to better monitor the performance your critical assets. Effective, affordable sensor technology provides 24/7 monitoring, and alerts can be sent to your CMMS for immediate action. Over time, you can accumulate data that can help your AI learn more about your assets and report potential threats. These reports can be the foundation for more effective, smarter business decisions.