Predict-It™: X-Ray Vision for Equipment Health and Diagnostics

Michael Santucci

CEO & Founder: Engineering Consultants Group, Inc. (Equipment Monitoring & Diagnostics, Plant Controls Engineering, Predictive Analytics, Evidence-Based Diagnostic Reasoning

Equipment Diagnosis – The Doctor will see you now.

Author: Michael Santucci


What if knowledge of typical equipment failure modes could be stored and combined with historical operating data? The potential for engineers to easily identify the probability of early-onset failure modes provides key insights into the risk associated with continued operation. Cutting-edge diagnostic technology in Predict-It™ reveals subtle changes in operating data and allows these symptoms to be combined with on-site observations; arriving at an accurate diagnosis of the failure’s root cause. Using optimal prescriptive maintenance actions helps target evidence-based diagnoses and achieves the lowest maintenance costs.

Let’s examine how maintenance has evolved and see how Predict-It™ leverages diagnostic insights for the highest potential savings.

Predictive Maintenance

Types of Maintenance

Reactive maintenance – With this method, repairs to equipment are made post-breakdown as a restorative solution. Fix it when it breaks. This makes sense for some assets where there is redundancy, for multiple parallel pumps running at low capacity factors, or for assets with little to no impact on plant output. Both the cost of instrumentation and the infrequency of common failure modes do not justify the cost of the sensory package required to monitor it.

Preventive maintenance – Changing your engine oil every 5000 miles or replacing your furnace filters once a quarter are forms of preventative maintenance. Original Equipment Manufacturers (OEMs) generally supply conservative recommendations for maintaining their equipment such as when to replace parts, service fluids, clean, etc. This type of servicing can be expensive and may also lead to premature removal of completely operational parts, all of which to avoid a less than likely catastrophe.

Predictive Maintenance – Predictive maintenance digitally monitors equipment health to identify possible failures long before traditional monitoring techniques. This technology enables a tailored maintenance approach rather than scheduled maintenance during overhauls. The primary goal of predictive maintenance is to precisely target conservation and repair efforts to avoid costly downtime. Advanced Pattern Recognition (APR) algorithms are deployed to identify sensors that are not operating as expected. These anomalies can be mapped to symptoms, the outward effects of common faults.

According to Allied Market Research, a market research and advisory company of Allied Analytics LLP, the global predictive maintenance market size was valued at $2.8 billion in 2018 and is projected to reach $23 billion by 2026, characterizing the importance that predictive maintenance offers to the industry, both now and—even more so—for years to come.

While the value of predictive maintenance is clear, a challenge is presented in how to distinguish between trivial issues and those of potentially catastrophic proportions. Power Generation companies and other heavy industries like the Oil and Gas sector employ Operational and Diagnostic Monitoring Centers (ODC’s) to monitor critical assets and digest asset anomaly data for the fleet. In monitoring centers, Subject Matter Experts (SME) are called to interpret the anomalies and provide a differential diagnosis of what failure mode(s) or mechanism may be the cause of the failure. However, these diagnoses can be somewhat subjective and lack a calculated estimation of fault likelihood.

Experts with years of industry experience have an empirical knowledge base to understand different types of alarms. This allows them to differentiate between a combination of symptoms to pinpoint a root-cause for many typical problems. As these experts retire or move up in an organization, the holes in collective knowledge leave an operation exposed to the learning curve of less-experienced engineers.

Predict-It’s Diagnostic Reasoner captures the knowledge of the SME and provides a uniform approach to interpreting fault probability. Prior occurrences of a given failure, statistical symptoms, and the strength of the causality between fault and symptom are the basis for determining the likelihood of the fault. This way, every instance of a likely asset fault is evaluated with the same metrics.

Networks configured for each asset type quantify the likelihood of each failure mode, given the presence of symptoms. These networks, known as Causal Asset Networks (CAN), allow the (ODC) analysts to perform “what-if” scenarios to better understand the significance of an APR alarm. In the network shown below for a coal pulverizer, color-coded faults are linked to both anomalies from the APR as well as plant observations. With each new piece of evidence, which takes form as an APR Alarm, we reevaluate the likelihood of each fault by the influence factor associated between the fault and symptom.

Once a network is configured, a novice analyst can immediately use the Diagnostic Reasoner as a guide to what fault conditions might be developing in the asset. The analyst understands the significance of the alarm and the likelihood that the condition is present based on the SME's prior configuration. With the CAN configured, the doctor is always in!

The Electric Power Research Institute’s Equipment Reliability process advocates for Risk-Based Maintenance (RBM), which is a further prioritization of threats at a unit level based on the probability of failures and their consequences. A risk matrix considers one entire generating unit as a system. Maintenance projects with high probability and high consequence shaded RED below have a higher risk of lost generation than those in Blue and Green:

Technological approach

Predict-It is ECG’s Advanced Pattern Recognition software that drives its Bayesian Diagnostic Reasoning Engine. Predict-It allows you to leverage the benefits of Predictive Maintenance which include:

  • Early Warding of anomalous operation
  • Improvement in equipment Availability
  • Reduction in maintenance expenditures
  • Full utilization of maintenance resources
  • Monitoring equipment lifecycle
  • Minimization of spare part usage
  • Maximize Profit!


  • Anomalies
  • Likelihood of Faults
  • Value of Evidence
  • Similarity of Prior Cases
  • Expected Sensor Values at Any Load or Production Level
  • Asset Variable Behavior
  • Precision of Model Estimate
  • Peer-to-Peer Discrepancy

Engineering Consultants Group can help implement the right maintenance plan for your operation. We provide industry-leading APR and Diagnostic Reasoning software. Join hundreds of satisfied customers that are currently monitoring their fleets of assets around the world with our robust solutions!

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