5 ways condition-based machine monitoring enables proactive maintenance and improves your operation
Proactive maintenance on your machines helps you prevent unscheduled downtime and minimize the productivity disruptions associated with scheduled maintenance.
When you perform proactive instead of reactive maintenance, you save costs and avoid the expanding ripple effect of downtime, which can jeopardize your ability to keep your customer commitments and put customer relationships at risk.
#1: Rely on real-time evidence
Proactive maintenance requires awareness and understanding of your machines and equipment. You need to monitor and document their condition and make the information available to the people who have the skills and resources to keep them running optimally.
Companies often use a combination of expert knowledge and historical machine data to anticipate possible machine breakdowns and initiate preventive repairs before a failure occurs.
That practice may be limitedly effective in avoiding unplanned downtime. It may also be wasteful, because you may use technician time and new machine parts long before they are required.
You also may inadvertently disregard a machine’s current conditions and emerging patterns that indicate a possible failure. When you perform condition-based monitoring based on real-time data, however, you stand a good chance of keeping your machine running and using your resources wisely.
Manufacturers and other production companies have begun gathering such real-time data by connecting their machines to the internet of things (IoT) by means of sensors that can record any condition at any part or component.
#2: Monitor performance and quality as well as potential failure
When you combine your real-time condition monitoring with machine histories and expert insights, you can take proactive maintenance and machine management to a higher level.
For one thing, you cast a wider net and consider other events and values than those that may lead to an outage. You don’t need to limit yourself to assessing and managing machine availability.
You can include quality assessments and verify whether production is within specified tolerances, or verify that throughput is in line with your goals and customer commitments. You can also identify patterns and trends that may affect a machine before its performance noticeably suffers or a failure becomes likely.
#3: Enable people to jump into action
Once you become aware of the critical thresholds beyond which a machine may fail, produce poor quality or lag in performance, you should set up automatic alerts based on industrial internet of things (IIoT) data to notify people when a machine is about to cross the line.
Your data will tell you if a vibration may have unwanted consequences, but your experts know what to do about it. They can make corrections that involve your machine directly, such as parts replacements or tuning. They can also engage with their colleagues to improve production planning and schedules or find a better rhythm for the inflow of materials and intermediate products.
#4. Optimize the machine environment
While your real-time condition monitoring makes it possible to see and remedy the causes of potential machine failures, poor performance or bad quality output, you can go outside of the factors that involve a machine directly.
There are probably other circumstances that could be monitored to avoid a negative impact on machine operation or eliminate waste and inefficiencies. IoT sensors can provide information about almost any conceivable condition.
For example, tracking a machine’s use of electrical power could alert you to spikes and increases in consumption. Long before a power unit fails, it may be worth replacing it with a new one that can save electricity and cost.
Environmental conditions – temperatures, airflow, particulates, fumes, moisture and others – can also have a detrimental impact on your machines. The payoff in assessing and mitigating them can be enormous.
#5: Track metrics that matter
Another way to make key decisions that enable proactive maintenance is to combine the data flows from multiple IoT sensors to simplify your analysis of real-time data or help you answer questions without investing time in research.
For instance, you can combine sensor data related to a machine’s availability, performance and quality in a single virtual sensor that indicates Overall Equipment Effectiveness (OEE), the widely used industrial metric.
You should create virtual sensors to provide evidence for any condition or measurement that is relevant to your business. For example, you could combine data streams about the package weight, temperature, moisture and container count of a food product to get a quality assessment.
Proactive maintenance can go beyond machine parts to include many other aspects of your operation. But you can always close the loop between real-time data findings from physical and virtual sensors and your proactive maintenance practices.
The more you learn, the more effectively you can improve the operation of your machines.