Director of Business Development
Rotating and reciprocating equipment is widely used across many industries and is typically designed to work around the clock in harsh environments. Failures can be frequent, time-consuming and costly.
Progress in condition monitoring and predictive maintenance strategies to address machine and component failures have been assisted by advances in IoT and Cloud Computing, allowing more data to be collected, processed, and analysed quicker and more efficiently, providing more significant insights into the data than ever before.
The value of condition monitoring and predictive maintenance strategies for rotating and reciprocating machinery is the ability to visualise and analyse an asset’s performance, wear and mechanical risks in near real-time. Sensor data such as vibration, temperature and running speed, collected and analysed, can determine if a machine or any of its components experience changes in performance while in operation.
Processing collected data at a high sampling rate and characterising the defects based on the signals can highlight any deviations from the normal operational range, raising alarms to operators and triggering an immediate response.
Predictive maintenance can be considerably more efficient and cost-effective than preventive maintenance tactics, wherein maintenance is performed on a regularly scheduled basis. The ability to respond to near real-time data is a significant advantage; it allows maintenance to be performed on an as-needed basis, thus reducing labour and material costs.
BMT DEEP includes these types of use cases seeking condition monitoring and predictive maintenance strategies for rotating and reciprocating equipment. What makes BMT DEEP an invaluable tool in this type of service is its ability to process high-frequency data – 10k Hz – in near real-time, allowing analysts the ability to manipulate, visualise, and analyse vast data sets and create fully customised reporting and alarming schemes to best suit their use.