Failure Data Analysis is a completely reactive program beset with many complications and trouble. We often hear, “our data is no good”, to which we reply – do you know why? We conquer bad data. We use many AI inspired tools to turn bad data into very good data.

With failure analysis of assets, Often the answer is deferred to someone who should do something better, deflecting the responsibility from the underlying failed design of the data collection of asset failure data. Most engineers do not seem to know when they have left truncated data, or right censored data, or informatively censored data, nor do they know how to deal with it, and which software program provides accurate results.

As the assumption of the underlying failure mode performance is the topic most maintenance strategy management and optimization programs are built upon, getting this part of your program wrong leads to under optimized maintenance, resulting in more than anticipated failures, which need more failure data collected.

We work with the world experts on this topic, and in fact DO know how to deal with the most complex of failure data correctly. More important is how to properly cleanse and analyze the data to obtain an accurate result. Far too often we learn of engineers throughout industry applying the wrong approach for their specific type of failure data, which of course generates incorrect results.

Failure Data Analysis shares important alignment with Survival Analysis used in medical studies, and clinical trials. With Survival Analysis we counter the challenges of medical studies with staggered entry, drop outs, and informative censoring using our advances, AI supported, analysis methods.

So is the understanding of the of the true reason for a loss of a patient.

If you are using a forward prediction program either for maintenance optimization of capital renewal decisions, or survival prediction in clinical trials, talk to us, the challenges of getting this analysis right, as they are complex, but we can guide you through the successful analysis and predictions needed.

Weibull Analysis is a statistical method used in reliability engineering to analyze failure data and predict future failures. It utilizes the “Weibull Distribution”, a flexible probability distribution that can model various failure patterns, making it a valuable tool for understanding and improving product or system reliability. By fitting the Weibull distribution to failure data through statistical regression, reliability engineers can predict failure rates, optimize maintenance schedules, predict the need for future CAPEX replacements, and improve product design. Click here to see more about Weibull Analysis in this post.