Lower Costs, Higher Output With Predictive Maintenance
Few breakdowns happen without warning. The challenge is spotting the signs early enough to plan and schedule repairs. Predictive maintenance services provide that insight.
Predictive maintenance services focus on the early detection of small changes in equipment operation. With enough data, these changes can be analyzed to reveal impending maintenance requirements, well before they affect production or machine uptime. Predictive maintenance equipment constantly monitors, records, and analyzes numerous aspects of equipment operation to provide deeper and more timely insights than have ever been available previously.
Predictive maintenance technologies include thermographic testing, vibration and oil analysis, and ultrasonic leak detection. Before there’s an obvious sign of impending failure, replacement parts can be put on the order and the work can be scheduled for a time that minimizes production losses.
A predictive maintenance program can supplement a preventive maintenance program. However, industrial machine predictive maintenance represents a more advanced approach with several differences from preventive maintenance. Most importantly, preventive maintenance occurs on a set schedule — whether or not equipment issues are present. A predictive maintenance plan allows for a more informed, data-driven, and effective solution based on advanced tools and predictive maintenance analytics.
The best predictive maintenance companies need to understand your operations, your equipment, and your production uptime requirements — in the near and the long term. Because predictive maintenance services are data and analysis-driven, expertise is necessary to implement the predictive maintenance equipment required to collect the important metrics, and to then implement meaningful actions from them.
Although there are learning and personnel curves involved in implementing a predictive maintenance plan, the efficiencies of a more targeted, more effective maintenance practice prove themselves worthy of the investment in predictive maintenance tools and processes.
A predictive maintenance program reduces unplanned production downtime by allowing better maintenance scheduling. It improves equipment safety and product quality through early identification of changes in operating conditions. Capacity increases when less time is spent on reactive maintenance and costs go down because there’s less need for overtime and rush orders.
Our predictive maintenance strategy uses data from predictive maintenance tools to drive asset management decisions. Traditional time or usage-based maintenance helps protect against breakdowns, but leaves the risk of doing too much, too little, or the wrong type of work. We begin by baselining current operating conditions. Then, regular data acquisition and predictive maintenance analytics start to reveal important trends.
Knowing what’s likely to happen inside complex machinery lets managers make better decisions. Spare inventories can be pared down instead of holding them for “just-in-case.” Decisions about scheduling equipment maintenance can be taken collaboratively rather than overriding production needs. By revealing the true causes of failure, the data from a predictive maintenance program can shape purchasing decisions, depreciation rates, and planned replacement dates.
Among predictive maintenance companies, Mano Enterprises has emerged as a leader due to our development of a comprehensive predictive maintenance services program, combining the latest predictive maintenance technologies with the processes and strategy necessary to make a real impact on your production efficiency.
Every predictive maintenance program Mano Enterprises puts together is tailored to the specific needs of each facility and customer. Inspection and evaluation by skilled technicians is a common thread running through all of our predictive maintenance solutions. That’s why manufacturers are hiring Mano Enterprises to implement predictive maintenance services, capture their valuable data and use it to make more aligned decisions affecting their machine uptime.