The 5 key features of predictive maintenance software

13 March 2024

Re-industrialization, recruitment pressures, rising energy prices, control over production facilities... These are just some of the challenges facing manufacturers today. Predictive maintenance solutions are part of this drive to improve industrial performance, providing concrete answers.

Analytics solutions that exploit process data already present in the plant are at the crossroads of two fields: data science and maintenance. Let's take a look at the five key functionalities of these tools dedicated to decision-making for operational staff.



1. Monitor your equipment in real time

A global, real-time view of the state of health of the entire production chain via a dashboard adapted to teams in the field.

The ideal solution is to have a monitoring system that combines two approaches: monitoring of known thresholds by business experts, and AI-based analysis of multi-sensor data to detect more complex phenomena, discover patterns that herald malfunctions and anticipate breakdowns. 


Customizable settings according to industrial constraints, process, team organization and notification preferences

Depending on the specific needs of each customer, the solution must be able to offer an (automatic) alerting system to provide field teams with all the information they need (what equipment, what problem, why, how to intervene) to facilitate the resolution of doubts and interventions. The aim is to provide a real decision-making tool that's easy to use, with all the information technicians need.

For optimum use of the predictive maintenance software, the interface must be dedicated to the industry.


2. Integrate Machine Learning technologies adapted to field challenges

AI technologies that are robust to the specificities of industrial data (raw, missing, multi-sourced, multi-formatted, different types, etc.)

Technology that is adapted to the reality of plant data is essential for performance, but it must also be scalable, continuing to learn over time to improve software performance over time. And that's where the added value of Machine Learning lies!

What does this mean for the user? The solution must involve the technician providing feedback on the relevance of the alerts issued by the software, and the analysis engine must be able to take this information into account automatically to update and continue training its algorithms.


Analysis reports with automatically generated customized indicators on equipment performance

Depending on its objectives and team organization, the solution must be able to offer performance reports on equipment health, adapted to the need (analysis granularity, business view, expert view, etc.).




3. Easy to set up and upgrade

Simple interfacing with existing company tools

Efficient at a given moment, the software selected must also be easily adaptable to the company's future needs. Opting for a scalable solution with integrated connectors and a software architecture capable of adapting to a large volume of data means adopting a long-term vision and surrounding yourself with a long-term partner and a scalable solution.


A solution compatible with cybersecurity constraints

Respecting the security constraints specific to each IT department is a prerequisite for adopting a solution that can be widely deployed across the entire machine population.




4. Reduce maintenance costs and optimize energy consumption

Optimize the industrial performance of your production facilities

An effective monitoring solution must be able to monitor and even improve equipment performance!

Optimizing machine settings to guarantee production stability, improving energy efficiency to ensure that equipment consumes just the right amount of energy without impacting cycle times... These are all central issues for manufacturers that can be addressed by a monitoring solution based on process data analysis and AI.


Diagnose malfunctions and anticipate breakdowns as early as possible

Real-time monitoring means anticipating problems! Predictive maintenance software must therefore be able to detect early warning signals of malfunctions using appropriate technologies, as mentioned above.

To be complete, the solution must be able to detect and diagnose problems linked to maintenance or energy overconsumption... And anticipate breakdowns over a time horizon adapted both to the industrial process and to user preferences.

The result: better planning of interventions, increased equipment availability, longer equipment life, improved use of resources (energy, spare parts stock, etc.).

  An AI-based equipment health monitoring solution provides a 360° view of all items: maintenance, production, energy consumption...


5. Sharing information

Centralize information

In the age of 4.0, dematerialization is becoming a real challenge for manufacturers. Predictive maintenance software enables information to be centralized digitally, so that a history can be kept of problems encountered, phenomena observed and interventions carried out. 


Create a knowledge base to facilitate interventions

Centralizing information gives you the keys to more rapid efficiency, with a knowledge base that's continuously enriched.

The benefits: operators in the field can upgrade their skills more quickly, and benefit from a decision-making tool that supports them on a daily basis as they become planners.

At DiagRAMS, we're convinced that these 5 features are the key to an effective analytics solution. Let's have a chat! Would you like to find out more about our projects and our predictive maintenance solution?

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