The blow molder conveyor motor at the bottling plant had been running steadily for years. It drove a belt that moved test-tube-like preforms from the husky to the next stage of the process, where they’d be expanded into plastic bottles.
It wasn’t the most expensive asset on the line, but data confirmed it was one of the most reliable: its steady pattern of vibrations showed consistent long-term performance. Recently, however, the company’s machine monitoring system tracked a sharp increase in the motor’s vibrations. The blow molder seemed to be suddenly, and rapidly, destabilizing.
The machine—and the line—were still running. But as the vibrations continued to increase, it was clear they needed to halt production and investigate. Anxiety rose as the line slowed to a stop… they had to locate the malfunction and correct it immediately… or turn the line back on and possibly risk catastrophic failure. If only they knew exactly what was wrong in the first place…
Understanding and Utilizing the Power of Continuous Data Streams
Advanced digital machine health solutions can interpret continuous data streams and provide actionable maintenance recommendations to protect assets and overall production. Continuous data streams can present great opportunities to increase productivity by—but they can also bog you down if you aren’t able to interpret or utilize the data effectively.
The secret to dealing with continuous data streams is knowing when to act. The right insights at the right time can mean the difference between missing and exceeding your production goals. If you halt production to inspect an asset and cannot locate the malfunction, you’re running on a double loss of production and labor costs. But with the right amount of advance notice and actionable machine health diagnostics for individual assets, spare parts can be ordered and repairs scheduled during planned downtime, minimizing the impact on production and output.
Catching intermittent conditions
To determine the ideal time for intervention, reliability professionals can examine a machine’s performance history using data collected on regular intervals. This continuous data stream makes it possible to catch intermittent defects and faults before they become catastrophes. Imagine a sudden fracture of a component, or a chip in a gear, that causes a sudden cliff change in an assets’ vibration data—the response time necessary for action is immediate.
On the other hand, defects caused by wear typically develop slowly and show a slow increase in vibration over time. Some defects take years to materialize, while others can develop over hours, days, or weeks. The rate of change can evolve over time, too, because a defect can develop faster and faster, like a wobbly wheel on a car. The data on these defects show as trends over time.
In either case, continuous data streams and digital machine health solutions are the best way to prevent and know exactly when to take action and more importantly, what to do to correct the detected malfunction.
Appropriate response times
Nobody wants to shut down a line unexpectedly. But In order to maximize the value of continuous monitoring and continuous data streams, response time is critical—especially when corrective actions are necessary.
Gradual, long-term detection is easier, even for slower algorithms. But fast-developing defects require a real-time data stream because they require a quick response. Without a sophisticated, and fast algorithm, you have less time to react when a machine fails. Even with only five minutes of advance notice, there may be an opportunity to shut the machine down and save the asset or production in some capacity.
Acting too early can also be an issue. Detecting a problem years in advance is possible, but it runs the risk of triggering unnecessary repairs, additional costs, defects in replacement components, or errors in the repair process itself. But don’t forget that any notification that arrives too late is just as bad as no notification at all. The ideal timeframe for when an intervention should occur depends on machine criticality, the detected malfunction, and the industry in which it operates.
Revealing assets’ full lifecycle
Reviewing data collected across an assets’ entire lifecycle allows experts to base future maintenance and repair decisions on the machine’s current and past malfunctions. This helps them plan accordingly and if a machine needs unique components, they can build in time to source the part. Or if a machine, like the bottling plant’s blow molder conveyor motor, has high production value but a low capital investment, they can plan corrective actions as a part of routine maintenance during the next shutdown.
Back at the bottling plant, the technicians had enough time to investigate the truth behind the vibrations: the conveyor motor belt was worn and some of its teeth were breaking off. They emergently ordered a replacement belt and scheduled time to replace the belt shortly after it arrived. Had the plant ignored the alarm, and the belt run to failure, the entire motor could have been compromised, along with the bottling line’s productivity.
Instead, continuous data streams—and a reliable digital machine health solution—demonstrated how actionable machine insights can keep production assets running at their very best, with the right information, at the right time.
Learn more about machine health, here.