For enterprises, sudden equipment failure is like a bug hidden somewhere. You know it exists, but you don’t know when it will appear. This kind of “anxiety” is a huge crisis for any enterprise.

How to solve this bug and turn the “danger” that may appear at any time into an “opportunity” for strategic planning has become the key to the problem – the choice of predictive analysis software has become a strategic issue for enterprises in this process.

01

The premise of eliminating bugs

Predictive analysis and “foreknowledge”

The concerns about potential crises such as unplanned downtime have led to a huge increase in market demand for predictive analysis software:

“According to relevant research data, in the next few years, the global predictive analysis market size will grow from US$12.5 billion in 2022 to US$38 billion in 2028, with a compound annual growth rate of 20%. ”

This broad prospect is due to the increasing maturity of artificial intelligence (AI) and machine learning (ML) technologies and algorithms, new methods for large-scale deployment of predictive analytics, and the availability and maintainability of data and systems.

Faced with the wide variety of predictive analytics software on the market, enterprises must consider variables such as return on investment (ROI) when weighing their potential deployments. The final choice of predictive analytics software “is hardware-independent”, “is it the best way to leverage existing software investments”, “is the solution easy to deploy, maintain and expand” and many other factors are all issues that enterprises need to consider.

02

Let the tools fully integrate

The power of human insight

Human experience and intuition play an important role in assessing potential crises, but due to differences in positions and departments, different employees are in different specific professional fields, and therefore have very different “experiences” in troubleshooting. For example, people from engineering or operations backgrounds often have very different ways of thinking when facing the same problem.

Although many predictive analytics solutions can provide abnormal alerts, if there is insufficient experience and insight, the imminent crisis may be ignored when facing abnormal alerts. Therefore, while enterprises use “human insight”, they must also acknowledge the subjectivity of the team in discovering and solving faults. At this moment, the advantage of “tools” is highlighted.

Rational and precise digital technology and human perceptual “experience” complement each other. It can “predict” – provide timely fault diagnosis alarms through real-time data, and “analyze” – explain the reasons for the alarm. The closed-loop predictive analysis strategy enables enterprises to collect, organize and analyze data, including real-time sensors, historical operations and financial impact analysis data.

Based on these data, users can immediately lock in the crux of the problem and let the anomaly disappear in the bud. More importantly, record and reuse relevant data to achieve continuous improvement.

03

The importance of fault diagnosis

But the “early warning” of the machine is not 100% accurate. Sometimes the alarm is not a warning of unplanned downtime, but just a sensor failure. This “oolong” greatly reduces the accuracy of the analysis. Unreliable data will cast a shadow of “wolf coming” on analysis and decision-making.

Therefore, excellent predictive diagnostics require “the right medicine for the right disease”, providing precise, real-time insights through customized data and diagnostic tools. AVEVA’s predictive maintenance solution estimates the time when a failure may occur through efficient and accurate fault diagnosis methods, helping companies accurately prioritize maintenance.

The prediction of the time of failure helps operations and maintenance teams “have a plan in mind” to determine whether to let the asset run until the next planned maintenance stoppage or initiate an emergency stoppage. This also enables the team to more accurately predict potential supply chain problems and consider the preparation time of spare parts. At the same time, prescriptive analysis can also provide actionable tasks to remedy the problem.

In this way, unplanned downtime is eliminated.

Conversely, relevant predictions can also help operators determine whether to postpone planned maintenance tasks. Plant personnel can schedule maintenance and assess risks more effectively, helping companies prioritize safety and profitability.

04

And “Hug” with Data

Make the most of data

As digitalization advances at a high speed, industrial companies are collecting more data than ever before. According to statistics, 50% of all industrial data was generated in the past two years.

With the help of various software and hardware, enterprises may have a large amount of data in their hands to monitor their assets reaching a certain threshold, such as temperature, heat rate, fuel consumption, power consumption, etc.

While these indicators may represent valuable insights, they are static. When conditions change, enterprises need to use multiple parameters to track and anticipate any deviations of assets. Therefore, a more dynamic production environment requires a more dynamic process.

In order to explore the potential of data to a greater extent, enterprises must establish a comprehensive data infrastructure “from engineering to operations to asset management to corporate finance” and use solutions that can integrate all relevant information sources.

AVEVA’s predictive maintenance software solution analyzes historical behavior, takes into account multiple thresholds and change patterns, and tracks the actual condition and real-time operating conditions of assets in real time to predict possible problems in the future.

This makes maintenance planning more effective, avoids over-maintenance of assets, and provides a clear view of the relationship between asset models, fault conditions, fault modes, sensors and actual fault matching information, so that everything is under control and strategic planning is possible.

05

AVEVA Predictive Maintenance

Make unplanned downtime disappear

AVEVA’s predictive maintenance portfolio has quickly become the industry standard. Combining digital twin technology with AVEVA™ Predictive Analytics (predictive maintenance software), it has opened up a new path for many companies from industrial fields, from power to chemicals to manufacturing, to improve operations.

AVEVA Predictive Maintenance Software is a code-free solution that does not require the support of software engineers or data scientists, and can be easily mastered by developers. AVEVA Predictive Maintenance Software provides advanced alarm and case management based on artificial intelligence technology, enabling knowledge capture and reporting. Built-in templates accelerate the configuration, deployment and expansion of the software to ensure maximum return on investment. Efficient and accurate fault diagnosis can accurately diagnose fault modes.

With AVEVA predictive analytics software, companies can diagnose equipment problems in advance by detecting subtle changes in real-time system operating data and normal operating archives, and diagnose problems days, weeks or months before equipment failure occurs, thereby avoiding unplanned downtime. A large number of implementation practices have also confirmed the feasibility and advancement of AVEVA’s predictive maintenance software:

Mitsubishi Electric Power

Using AVEVA’s predictive maintenance software to improve the operational awareness of its energy system, it has achieved outstanding results in preventing unexpected downtime.

Duke Energy Corporation of the United States

Using AVEVA’s predictive maintenance software to centrally monitor its power generation assets, it maximizes the safety, reliability and production performance of assets. After the platform went online, it saved more than $34 million in just one early warning.

Siam Chemical Group of Thailand

With AVEVA’s predictive maintenance software, the reliability of the plant has been increased from 98% to 100% through sustainable real-time monitoring of equipment activities, avoiding equipment asset failures. This saving is equivalent to a 9-fold return on investment.

AVEVA Predictive Maintenance Software

Based on an asset library derived from more than 22,000 hours of experience, AVEVA Predictive Maintenance Software allows users to understand “how long until a failure occurs” and “which problem should be solved first”, comprehensively improving the prediction ability, effectively detecting enterprise performance problems and predicting their asset failures, helping enterprises transform from reactive maintenance to proactive predictive maintenance, and making unplanned downtime disappear.
Related product recommendations:
ABB 3BHE041576R3011 – PPD517 A3011
ABB 3BHE041576R3011 PPD517 A3011
GE IS220PPDAH1B
ABB PPD113B01 | 3BHE023784R1010
ABB 3BHE020570R1022 PPD114 B1022 AC 800PEC 
ABB PPD113B01 | 3BHE023784R1010
ABB PPD115A02 | 3BHE017628R0002
ABB 3BHE020455R0001 PPD103 B01
ABB PPD113 – 3BHE023584R2634 AC 800PEC Control System
Honeywell TK-PPD011
ABB PPD113B03 3BHE023584R2365
ABB 3BHE020455R0001 – PPD103 B01
ABB PPD113 3BHE023584R2634
ABB PPD113 B01-26-111000 AC 800PEC
ABB PPD113B03 3BHE023584R2365
GE IS220PPDAH1A REV E
GE IS220PPDAH1A
ABB PPD113B01-10-150000 | 3BHE023784R1023
ABB PPD113B03-26-100110 3BHE023584R2634
ABB PPD115A102
ABB PPD113B01-10-150000
ABB PPD512 A10-15000 
More…