The 3 Major Challenges of a Predictive Maintenance Strategy and their Solutions

What obstacles do business experts face when implementing a predictive maintenance strategy? How can they be resolved?

What obstacles do industry experts face when implementing a predictive maintenance strategy? And more importantly, how can these challenges be overcome?

The 3 major problems of a predictive maintenance strategy and how to address them.

Recap on Predictive Maintenance

Predictive maintenance represents a significant shift from previous maintenance strategies. While preventive maintenance improved upon reactive maintenance, it lacked the reliability, security, and performance needed for today’s industrial sector. 

Predictive maintenance, driven by artificial intelligence and IIoT, has become an essential solution adopted or planned by every production site. However, despite its undeniable advantages, there are concerns among maintenance managers. 

We have identified the 3 main problems surrounding the implementation of such a strategy and provide the necessary tools to overcome them : 

Data Diversity

Data forms the foundation of any predictive maintenance strategy. However, implementing predictive maintenance across an entire production site can be challenging due to the diverse nature of the data to be processed (sound, temperature, vibration, electrical load, etc.). 

Different types of sensors may be required to collect these various data types, which can incur significant costs. It is crucial to identify the assets where predictive maintenance implementation is a priority based on their cost implications or propensity for malfunction. 

Once these assets are identified, determining the most relevant indicators to monitor for each of them is essential. Furthermore, having software capable of interpreting the collected data is crucial. 

DiagFit, a predictive maintenance software, is sensor-agnostic and adaptable to various equipment types, enabling it to process diverse data. Its role is to build a predictive model based on healthy equipment data, without the need for historical failure data, to detect anomalies indicating faults or aging components.

High Costs

The implementation of a predictive maintenance strategy incurs costs, including sensor deployment, software acquisition, and team training. However, this investment should not deter industry players from adopting it. 

Firstly, DiagFit offers a no-code interface, making it accessible to maintenance teams with minimal programming skills. Secondly, the initial investment should be viewed in relation to the significant long-term cost savings enabled by predictive maintenance. 

According to a study by the U.S. Department of Energy, such a solution can reduce maintenance costs by up to 30% and yield a minimum 10x return on investment. Its rapid implementation allows teams to quickly enjoy the benefits, such as optimized interventions, reduced unplanned machine downtime, improved accuracy, increased productivity, and reduced stress.

Lack of Security

The migration of information systems to the cloud has made the industrial sector more vulnerable to cyberattacks. Previously, attacks could lead to localized information theft or compromise a portion of a production site, but now, hackers can jeopardize the operation of multiple industrial sites with a single strike. 

Data security is a central concern in the industry 4.0 era. However, these concerns can be addressed. DiagFit is designed with strict adherence to security recommendations to preserve the integrity of processed data. Moreover, predictive maintenance can be employed to prevent security vulnerabilities that may pave the way for cyberattacks, thus contributing to the protection of enterprise information systems.

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