Deviation of Optimal Parameter Alerts in Decanters, Tricanters, and Centrifuge Separators
Overview: Preventing Downtime with Anomaly Detection Machine Learning
Efficient anomaly detection machine learning can be the difference between minutes of analysis and hours or days of damage control. Companies rely on accurate data to predict and address machine malfunctions before they occur. When inaccurate or irrelevant data is utilized, the chance for error is much higher and can result in delays or even machine failure. This is expensive and time consuming.
In order to predict problems before they occur, a business must invest in a proper data analytics software and data management system. Predictive maintenance machine learning is a valuable asset to have, as it determines anomalies, reports them, and addresses the root cause of a problem so it doesn’t occur again.
By utilizing anomaly detection machine learning, a company can work more efficiently. Machine downtime is minimized, and destructive threats are anticipated by AI as it learns how to effectively deal with problems as they arise. In addition, data analytics software collects the data and adapts it to visualizations that plant operators and managers can easily understand. This data can easily be put into actionable resolutions, working alongside automated machine learning.
Challenge: Detecting and Alerting Deviations in Optimal Parameters
Fishmeal manufacturer, Fiordo Austral, needed real-time data assessment and visualizations to monitor for proactive alerts throughout their decanters, tricanters, and centrifuge separators in their production lines. Assessing data and utilizing it for problem-solving is a tedious and difficult task when done manually. You lose manpower, money, and time. Not to mention, manual recording and entries of data are susceptible to human error, which only requires more money and time to fix.
Fiordo Austral was specifically looking for a data analytics software to pair with anomaly detection machine learning to unleash proactive alerts when there was a deviation of optimal parameters in their decanters, tricanters, and centrifuge separator such as Flujo Entrada [m3/h], Temperatura Entrada [°C], RPM Tambor [RPM], Corriente Tambor [A], Torque Tornillo [%], Velocidad Diferencial [RPM] and Vibraciones [mm/s].
In the manufacturing industry, it is crucial that anomalies are addressed. They can lead to bottlenecks, quality control issues, and wasted product or worse, physical harm.
Without accurate data, Fiordo Austral couldn’t work efficiently and predict problems. However, those predictions cost time as well. Predictive maintenance machine learning tools can lessen the burden placed on their plant.
Solution: Continuous Vibration Monitoring and Proactive Anomaly Detection
Sightline worked together with Fiordo Austral to establish anomaly detection machine learning in their production plants. Triggers in Sightline EDM were set up, and alerts and varied responses were created to find anomalies in their decanter, tricanter, and centrifuge separator parameters. Once identified, Fiordo Austral operators are alerted in real-time by Sightline’s EDM software, helping them find the anomaly and determine the root cause quickly.
Continuous vibration monitoring is an important facet of Sightline’s process when observing the decanters, tricanters, and centrifuge separators. By monitoring anomalies in the vibration of the manufacturing machinery, Sightline’s risk-based analysis platform alerts plant managers to potential problems in the machines. This prevents machine downtime and increases productivity throughout the fishmeal manufacturer’s production line.
Results: Predictive Maintenance Machine Learning Optimizes Productivity
With Sightline’s team dedicated to the pursuit of solutions supporting Fiordo Austral’s business and decision-making requirements, Sightline EDM optimized Fiordo Austral’s manufacturing and industrial plants.
With root cause analysis, our machine learning gets to the source of a problem and addresses it before it can occur. For food and beverage companies, this is crucial because entire batches of product can be forfeited without predictive analytics assessing bottlenecks, machine downtime, and other inefficiencies.
Machines are seamlessly maintained through real-time data collection and utilized historical data. Sightline EDM identifies anomalies within minutes to save plants hours of downtime and lost productivity. This boosts the efficiency of a manufacturing plant, and fast solutions keep your equipment running in top form.
