Manufacturing systems and processes are becoming complex, which means rational decision-making in process control is a necessity. It stands to reason that better information gathering and analysis techniques are needed for effective condition-based monitoring.
This monitoring is where the measurable condition(s) of a machine are continuously monitored by checking pre-defined parameters such as temperature, vibration, oil levels and other, similar, measurements, which enables the tracking of patterns that help predict equipment failure.
In turn, this ability to predict allows for timely action and prevents major or catastrophic failures later on. Condition monitoring is a critical component of preventive maintenance because it always ensures that equipment is always properly maintained by checking the variance of the actual parameters that are being monitored.
Another benefit of condition monitoring is that it allows users to work out when their equipment is nearing the end of its life, and enables a smooth replacement plan.
“Machine monitoring across multiple channels/platforms can significantly increase the probability of detecting failures in a machine. For example, vibration analysis on a motor might not be enough. The customer will also require temperature monitoring to find out more information about the machine,” said Jas Singh, systems and solutions manager for ifm efector.
When it comes to the Internet of Things (IoT), the lifecycle of technology can be monitored in real time and continuously using four main areas:
- connected devices and sensors;
- ubiquitous data networks;
- cloud storage; and
- big data processing.
In other words, everything connects and then communicates its status back to software platforms.
These cloud-based software platforms are able to swiftly process information and provide a range of insights, which is a direct requirement when it comes to predictive maintenance.
Some of the most critical industries where condition monitoring is needed include manufacturing, utilities and transportation, where the need for up-to-date information is a must for the safe and smooth running of the given process.
According to Singh, monitoring equipment, such as vibration monitoring will generate massive amounts of data that can be sent to the cloud and then analysed, which will help predict the failure of the machine.
In the asset-intensive manufacturing, utilities and transportation industries, success is dependent on the safe and reliable performance of those assets.
By capturing and analysing more complete operational data, analytics can help these industries manage and maintain their assets to improve safety, performance and equipment life.
With the IoT, the volume of data is so huge that data collection and its integrity are issues that need to be thoroughly assessed for any condition-based monitoring regime to be effective.
Standardisation of data is one way to ensure that data coming from different sources tell the same tales.
Thomas Davenport, analytics guru with Babson College and MIT, urges enterprises to hold device manufacturers accountable.
“First, there should be rigorous calibration before the device leaves the factory, and an on-installation calibration routine to ensure that the device works as expected,” he said. “Second, ongoing calibration is required to make sure the device continues to work properly.
“Ideally, the installation and ongoing calibration routines should be built-in and automated.”
Ultimately, Davenport cautions, “you should not expect perfection, particularly with new devices. But you must insist on rapid improvement”.
“[The IoT] will enable appropriate conversion of this data into information,” said Singh. “The tools available will be able to give us much more information than we have right now, and in the end, enable smart decisions to be made. In our eyes, the IoT will be an enabler.”
When it comes to condition monitoring, energy efficiency and data transportation, ifm has released the SmartObserver suite for a range of industries but especially for manufacturing, utilities and transportation.
Engineered for the continuous condition monitoring of machines and systems, SmartObserver is designed to provide trend analyses, limit values, evaluation of all process parameters, data acquisition, evaluation to DIN ISO 50001, visualisation and evaluation with trend displays.
These trend displays can include pressure, flow, temperature, rotational speed, the operating conditions of machines, the organisation and planning of maintenance tasks, data provision for higher-level systems and alarm escalation.
According to Singh, ifm’s SmartObserver is highly suited for condition/vibration monitoring in most industries including mining, water, food manufacturing and also utilities monitoring systems.