Condition monitoring prevents unplanned downtimes in transport systems

Transport systems such as escalators, conveyor belts, overhead conveyors or stacker cranes cannot tolerate downtime. Suitable condition monitoring could prevent unwanted downtimes at an early stage; conventional vibration analyses are not suitable for the very low-frequency vibrations of these systems, though. HARTING and Formsmedia GmbH have worked out a new concept for condition monitoring which has already proven itself in practice.

Unplanned downtimes are a recurring and expensive nuisance in transport systems. Monorail conveyor transport systems used in automobile manufacturing show this in an exemplarily way. The supporting structures transport heavy components or whole vehicle bodies. The whole load is born on plastic coated wheels. At some point the coating will come off the wheels and the monorail conveyor will have wheel damage. Production is interrupted and repair of the monorail conveyor is time-consuming.

Condition monitoring for a monorail conveyor transport system also available as a retrofit

Vibration analyses for fast running engines or transmissions have proven themselves as a reliable monitoring instrument for about 20 years. Slow moving transport systems such as monorail conveyors require a considerably more sensitive vibration detection in the milli-G range due to weak and very low-frequency vibrations. Formsmedia, a company for measurement technology, pulse and data analysis, has developed a monitoring solution with HARTING for this requirement:

• Highly sensitive sensor boxes with MEMS acceleration sensors capture the movement of the transport system capacitively and detect the vibrations at the wheels, collect data on the motor current and on the temperature of the drives.
• The sensor data is collected and transmitted via Modbus to the Edge Computing System MICA. The MICA is a networkable and secure mini-computer with a Linux based operating system and a virtualized application environment consisting of Linux containers.
• For condition monitoring of the transport systems, the MICA uses an analysis software from Formsmedia to aggregate, store and visualize sensor data included locally onsite. MICA is suitable for assessing data quickly directly onsite so that no unnecessary data needs to be passed on.
• The fast Fourier transform (FFT) is used to analyze the spectrum of the vibration and to evaluate the non-harmonious vibrations as well.
• The condition monitoring system can also be added on for transport systems with slow moving components as a retrofit.
• The data is transmitted to higher-level SCADA control systems or cloud based IoT platforms to carry out condition monitoring of several systems and provide extended functionalities, such as predictive maintenance.

Condition monitoring for difficult environment conditions

The condition monitoring of transport systems must be frequently set up under difficult environment conditions. There is either not enough space, the distances that need to be bridged are large or there is dust, heat and humidity. The sensor boxes and MICA can be used in a rough environment and are also protected against heavy EMC loads to degree of protection IP 65/67.

All local requirements are recorded in detail in a proof of concept to preconfigure the sensor box and MICA for installation. The attachment of the sensors is also checked for mechanical suitability so that weak vibrations are recorded reliably. A Modbus checkout program can be used to double check all the preset functions on-site during commissioning and ensure the correct measurement ranges. This means false alarms can be largely be ruled out.

An early warning message prevents plant downtimes

Threshold values are defined for evaluating the sensor data so that the production engineering department receives a message about the early need for maintenance. In practice, it has proven helpful to install a monitor on-site that is connected to MICA via Modbus RTU (Remote terminal Unit). The current characteristic values and critical system states are displayed visually on the monitor in the form of a traffic light function. Selected receivers are also sent an alarm message if threshold values are exceeded. In addition, employees on-site have the possibility at the touch of a button to send events such as breaks, malfunctions, or missing material to MICA. This means the sensor values can be connected to real operational events.

The condition monitoring solution can be enlarged to include machine learning for predictive maintenance in order to predict when the next maintenance service is required. Cloud services are generally used for the necessary analysis of historical and current data. Machine-learning algorithms are developed and trained for this using the collected data about the vibrations of the wheels, the fluctuations in the motor current as well as the temperature development of the drives. Predictive maintenance is capable of recognizing slight changes in the sensor data as anomalies and able to reach conclusions about the current conditions and forecasts about future conditions.

For topics such as predictive maintenance, it is recommended to engage in cooperation with specialized IT system houses who have experience in the area of data analysis and who develop algorithms, rules and dashboards individually to the user's requirements and are capable of integrating it into existing control or ERP systems. The MICA.network was founded for this by HARTING with IT companies.

Quick amortization of the condition monitoring solution

The cost of launching a condition monitoring system pays for itself relatively fast. Thanks to a standardized condition monitoring concept, the planning, system and installation costs are low and the benefit to the system operators is high. This means that wear of critical components can be recognized early and a failure of the transport system avoided. This increases system availability and the overall equipment effectiveness (OEE). In addition, fewer repairs and as-needed maintenance reduce the maintenance costs. This ultimately improves the service for the customers concerned/fields of use.

An additional effect of condition monitoring can be seen in a retrofit situation. Thanks to digital monitoring, old facilities can become state-of-the-art again. The maintenance cycles, which are otherwise more frequent due to the product lifecycle, can be reduced down to the actual requirement, thereby extending the usage time of the facility.