Major industrial players share a common challenge: minimizing maintenance costs whilst pre-empting equipment failure. Accomplishing these two objectives, often at odds with each other, is further complicated by the variance displayed across equipment failures: low-impact, high-frequency incidents require different remedial action than infrequent, yet catastrophic breakdowns.
BYES Predict offers a solution to this recurrent challenge. First, a wide range of data, from vibration patterns to energy use, is collected over time for industrial site equipment, using both smart sensors and physical maintenance crews. Next, this data is combined on a digital analytics platform, using advanced predictive algorithms in order to identify potential issues, schedule physical inspections and maintenance interventions.
BYES Predict relies on a network of autonomous sensors connected via the LORA network, and therefore compatible with a wide range of industrial equipment including older technologies. The predictive algorithms are regularly reviewed to enhance accuracy and enrich the insights delivered to clients.
BYES Predict helps industrial clients pre-empt equipment failures, enhance resilience and optimize maintenance schedules, delivering considerable cost savings, as well as avoiding incidents.
Insights gained from observation are used to make better decisions ranging from maintenance patterns to energy use, as well as investment in new technology. The combination of physical sensors and digital analytics delivers a powerful tool to understand and control maintenance across industrial sites.
Furthermore, technicians benefit greatly from the predictive maintenance platform, which provides them with a complete picture of each piece of equipment and frees up time previously spent on physical inventory inspections for individual case by case.
Data from the BYES – Safran Landing Systems collaboration is highly encouraging:
- 8 material breakdowns per year avoided on average since 2016
- 600 hours per year saved on prevention inspection rounds
- 30,000 EUR / year maintenance cost savings
Data from another industrial site is equally encouraging:
- Early detection of issues for a critical piece of infrastructure (rolling bridge) enabled the prevention of incidents and saved considerable time and costs related to a potential breakdown
- 30,000 EUR savings: usage rate data analysis helped avoid the purchase of obsolete technology
Level 1 : Proof of concept or lab test
Level 2: Tested under real life conditions
Level 3: Commercial solution