The very least an organization can do, within reason, when it comes to managing an unplanned downtime event is to record that it happened and manually determine what may have caused the failure by painstakingly scouring through the data. While conducting a root cause analysis is good behavior, from a corporate culture perspective, it is time-consuming and may not produce the most accurate storyline.
Fortunately, by applying TwinThread’s Predictive Operations Platform and its purpose-built predictive uptime application, finding out the reasons for failure is efficient, accurate, and easy. The predictive uptime application will automate the diagnosis of your downtime event, allowing your team to identify the issue faster and correct the factors that contributed to the failure in the first place.
However, what predictive uptime can do for your operation doesn’t end there. As the Predictive Operations Platform learns from your data, it will begin to provide advanced warnings of unwanted events that may occur if not mitigated. Along with these advanced warnings, the predictive uptime application will offer preventive recommendations that inform your operators how best to reduce or eliminate the threat of downtime.
Whether the predictive uptime application is supporting your team in looking back - determining the cause of failure - or looking forward - seeing and preventing a downtime from occurring - it’s providing more and more value as it is drawn on.
TwinThread’s predictive uptime application applies several methods in allowing the user (operators) to achieve a truly forward-looking and preventive approach.
The first is downtime probability. Quite simply, the application draws on the extent of your data to predict what the chances are that a downtime event will occur in the future - if the current processes go unchanged.
Next up is model anomaly score. Once TwinThread establishes a baseline for what is “normal” or expected performance from your assets or equipment, it can begin to identify deviations from this baseline.
Where TwinThread informs your team that there is a high probability of downtime and your model anomaly score is not in a “normal” range, the predictive uptime application provides analysis via its ideal conditions feature (and also visualizes this insight through its ideal conditions visual). Ideal conditions will provide your team with specific process changes that will allow them to prevent a probable failure, while the ideal conditions visual will offer a real-time representation for the performance of current processes and compare them against the optimal processes being recommended.
Lastly, the predictive uptime application offers insight through its anomaly timeline and anomaly breakdown features. Anomaly timeline sets the stage for evaluating anomaly breakdown by trending model anomaly scores. While anomaly breakdown provides a prioritized list of inputs that were (are) contributing to the probability of downtime for that particular window of time.
Getting Started with Predictive Uptime is Easy
Now that you’ve read about what it is and how it works, see it in action for yourself. Click below to schedule a demo and our team will show you how simple and fast it is to begin preventing downtime within your operations.
Tags:Article, Blog, Factory, Autonomous Operations, Operations, Monitoring, Downtime, Uptime, Alerting
February 2, 2021