Integrated Data Acquisition & Logging in Smart Linear Motors for Industry 4.0

Predictive Maintenance Actuators with Onboard Force & Position Sensors 

This paper explores the role of integrated data logging in advanced manufacturing and industrial automation, autonomous systems, product development, and lab testing. It outlines how built-in logging capabilities enable predictive maintenance, process optimization, and faster iteration by turning operational data into actionable insights. Using Iris Dynamics’ ORCA™ Smart Linear Motors as an example, we demonstrate how combining actuation, sensing, and logging in a single platform reduces complexity, cuts costs, and increases system reliability.

Data logging is central to modern engineering, providing insights needed to optimize performance, prevent failures, and refine designs. It involves the capture and storage of operational data for later analysis. It enables informed decision-making, early fault detection, and continuous performance improvement across sectors. While many systems require external hardware to collect this data, the ORCA™ Series Smart Linear Motors integrate position and force sensing, force feedback, and high-speed actuator communication at up to 2.25 kHz directly into the magnetic linear motor. This integration streamlines setup, reduces points of failure, and accelerates the path from prototype to production.

Definitions

Data Logging: The process of capturing and storing data, generally with the goal of later analysis. Data logging is used in a variety of environments such as industrial process control, environmental monitoring, scientific research, and quality assurance [1].

Data Acquisition (DAQ): The process of sampling and recording the physical characteristics of a system, often as part of data logging [2].

High-Frequency Sampling: A process in which a DAQ system rapidly converts a continuous input signal into a set of discrete numeric values, within intervals of milliseconds or less, that approximates the initial input [3].

Closed-Loop Control: A control method that continuously adjusts system operation by monitoring the output, comparing it to a desired setpoint, and modifying the input to minimize discrepancies, thereby enhancing system accuracy and reliability [4].

Benefits of High Frequency Data Logging in Smart Linear Motors

Predictive Maintenance: Logging key operational data such as force, position, speed, acceleration, temperature, power, and voltage allows early identification of trends that indicate wear or degradation within the system itself. This enables your team to act proactively, addressing issues before they lead to failure and cause downtime, disruptions, or potential safety hazards. Predictive maintenance optimizes maintenance schedules, reduces repair costs, and extends system lifespans.

Pre-Emptive Downtime Reduction: While predictive maintenance focuses on long-term wear trends, pre-emptive downtime reduction addresses sudden, short-term anomalies that can threaten operations. By detecting unexpected changes in performance, such as spikes in force or irregular motion profiles, teams can take immediate corrective action.

Optimizing Control Systems: Data logging provides the real-world feedback needed for tuning PID controllers and other control systems. Rather than relying solely on theoretical or simulated models, engineers can use actual system data as input to closed-loop controllers. This enables more accurate tuning of PID parameters, resulting in improved stability, responsiveness, and efficiency under real-world conditions [4, 7].

Research & Development Support: During development, engineers can easily migrate between different ORCA motor models and collect data to evaluate which model best suits the needs of their application. This can be achieved by oversizing an ORCA motor, collecting data while it runs, and making a data-driven decision regarding the most suitable size for mass deployment.

Features of a Smart Linear Motor with Embedded Sensors for Data Logging

1. Integrated Onboard Sensors: ORCA closed-loop servo motors are equipped with onboard sensors that collect real-time data from the surrounding environment and the motor’s internal state. These sensors capture critical parameters such as position, force, temperature, and velocity, with the shaft’s position derived from magnet alignment relative to the stator. This provides the foundation for reliable performance monitoring.
2. High Sampling Rates: The data collected by the sensors is sampled at high frequency, allowing for fine-grained insights into system behaviour. In a high-speed linear actuator, this capability ensures that rapid motion changes are accurately captured. This raw analog input is converted into discrete numerical values that closely approximate the original signals, enabling accurate tracking of dynamic events [3].

3. Fast Communication Speeds: ORCA motors can transmit data to external systems at rates of up to 2.25 kHz. This high-speed communication allows users to stream large volumes of data with minimal latency, supporting responsive closed-loop control and detailed analysis.

4. Flexible Data Logging Tools: Data logging is accessible through built-in tools available in the orcaSDK and IrisControls software. These tools allow users to define their own logging workflows and export data in common formats such as CSV or TXT for further processing, visualization, or archival.

   

How to Log Data from an ORCA Motor in Real Time

While the actuator is operating, data can be logged through one of two interfaces: the graphical user interface IrisControls, which supports a plug-and-play connection via USB, or the OrcaSDK, which provides an abstraction of the Modbus communication protocol along with built-in logging capabilities, as shown in Figure 3. Embedded systems and PLCs can achieve even higher communication rates, exceeding two thousand messages per second. For short video tutorials on extracting data from your ORCA motor, refer to our Iris Tutorials video.

Fig. 3. This example logs the time and position in position_log.txt. It is written using pyorcasdk, the orcaSDK in Python [5].

To capture data, the orcaSDK offers a number of functions, such as:

get_force_mN()

which can be used in the following manner, to read data, including force, position, temperature, voltage, speed, and acceleration:

Fig. 4. The ORCA can capture data related to force, position, speed, acceleration, temperature, power, and voltage. This code is written in C++ using orcaSDK [5].

Once the data is captured, it can be graphed using tools like Excel, IrisControls, or using software libraries such as Matplotlib, as shown in Figure 5.

Industries Where Data Logging is Delivering Value

Manufacturing & Industrial Automation

In the era of Industry 4.0, data-driven decision-making and big data analytics are cornerstones of smart manufacturing, where connected systems continuously monitor, analyze, and optimize manufacturing data for improved productivity. One key trend is the use of advanced data collection through sensing technologies that facilitate direct, real-time measurement of machine and process data [8]. In this sector, data logging enables precise measurement of actuator performance under varying loads and speeds, helping refine motion profiles and improve control algorithms.

The framework for data-driven manufacturing, as shown in Figure 7, is organized into four interconnected layers [8]. In the manufacturing layer, production equipment (actuators, conveyors, robotics, and other systems) executes the physical process while feeding operational data to the data layer. Data logging occurs in the data layer, where high-resolution data is gathered. In the knowledge layer, raw data is processed, features are extracted, and patterns are identified through advanced analytics or machine learning models. Insights are then passed to the decision layer, where they are used to model behaviour, assess performance, and forecast manufacturing trends. This structure supports informed, strategic decision-making across production systems [8].

Data logging supports a wide range of applications within manufacturing, from predictive maintenance, which tracks vibration, force, and temperature trends to detect wear early, to quality control, where positional accuracy, cycle times, and force profiles ensure parts meet specifications. It also enables process optimization by identifying bottlenecks, supports traceability through recorded production parameters, and accelerates root cause analysis after faults. Logging further informs energy management, helping reduce operational costs and improve sustainability. The importance of data-driven design is evident in the digital twin framework, which continuously synchronizes the physical system with a virtual model for predictive analysis and fault detection [9].

Autonomous Systems

Within autonomous systems, data logging plays a critical role across a range of applications. These include monitoring key parameters such as temperature, pressure, and environmental conditions by using an actuator with onboard sensors, including a position sensor and integrated force sensing, to capture data directly at the source. High-resolution positional tracking of robotic joints, end effectors, and mobile platforms allows engineers to fine-tune motion profiles, reduce backlash, and improve repeatability. Force sensing and torque data can be captured during interactions with the environment or payloads, enabling optimization for efficiency and safety. In autonomous and semi-autonomous systems, logging can validate sensor fusion algorithms by correlating motion system data with vision, LiDAR, or GPS inputs. In collaborative robotics, it supports real-time safety compliance by monitoring joint speeds, collision forces, and workspace limits.

Product Development & Lab Testing

Data logging is a powerful enabler of innovation in prototyping, testing, and experimentation. By continuously capturing real-time performance data, researchers can refine system designs and experimental setups based on observed results rather than assumptions. This iterative, feedback-driven process accelerates development cycles and improves final outcomes. The collection, storage, and analysis of sensor data are especially valuable in field research, where direct observation can influence results. Researchers involved in environmental monitoring can use data logging to study trends related to water, air, and soil quality, as well as humidity and wind speed [11]. Sensors provide objective, quantifiable measurements that are unaffected by perception or interpretation, ensuring system responses reflect actual operating conditions rather than subjective impressions. Across industries, data logging allows researchers to identify patterns, validate hypotheses, and troubleshoot anomalies. High-resolution operational data supports stress testing, endurance trials, and system optimization.

For instance, smart linear motors with integrated data logging and sensing capabilities, such as ORCA motors, are used in production line testing systems, where multiple motors apply precise loads onto weight scales for product validation. They were chosen for this application due to their integrated force and position sensing, eliminating the need for external sensors and a complex system. Their inherent force control enables the motors to sense and adjust the force they apply in real time, a crucial feature for applications requiring precise load application.

Aerospace & Defence

In aerospace and defence, data logging is a mission-critical capability that underpins testing, validation, and operational readiness. High-fidelity logging systems capture transient forces, vibration signatures, and positional accuracy during both simulations and live field trials, allowing engineers to verify system performance under extreme conditions such as rapid acceleration, high loads, or thermal cycling. These datasets support the validation of flight control systems, propulsion units, and precision actuation mechanisms, ensuring they meet demanding safety and reliability requirements.

For example, ORCA motors with integrated sensing have been used in aircraft structure fatigue testing systems, specifically to simulate weight-on-wheels signals that demand low latency and real-time responsiveness. ORCA motors were chosen for their inherent force control, which allows for precise force application, a crucial factor in replicating the stresses experienced by aircraft structures during takeoff, landing, and ground operations. Their ultra-low-latency response ensures that the motors can rapidly adjust to dynamic testing conditions, providing accurate and reliable data for fatigue analysis.

Where Linear Motors Excel in Data Acquisition Logging

 ORCA Series Smart Linear Motors save engineers time and money compared to traditional pneumatic or electromechanical actuation systems. The ORCA mechanical linear actuator integrates sensing, control, and data logging, which eliminates the need for additional external systems such as drivers, sensors, load cells, or the data logger itself. This reduces system complexity, minimizes wiring and integration effort, and improves reliability by reducing points of failure.

The ORCA software development kit (SDK), available in both C++ and Python, abstracts the Modbus communication protocol, enabling developers to interact with the motor through intuitive, high-level commands rather than low-level register calls. This significantly accelerates prototyping and reduces the learning curve for integration. Using the SDK, users can perform high-resolution force and position data capture directly from the motor, capture real-time motion, force, and positional data, automatically log it to a text or CSV file, and feed it into analysis tools for visualization or machine learning workflows.

Final Thoughts: Data Acquisition Motors Driving Industry 4.0

Integrated data logging in ORCA™ Smart Linear Motors transforms how engineers monitor, optimize, and maintain complex systems. By combining actuation, sensing, and high-speed logging in one unit, these motors eliminate the need for external hardware while delivering rich, actionable data. This capability accelerates development, supports predictive maintenance, and ensures consistent performance across manufacturing, robotics, and research environments by delivering a precision motor control system with integrated sensing and logic. As industries move toward Industry 4.0 and demand smarter, more connected systems, the Industry 4.0 actuator with integrated logging stands out as a practical, scalable way to turn operational data into improved performance.