Custom machine monitoring system for smarter manufacturing operations
Build production resilience with monitoring that detects equipment issues early and enables proactive maintenance.
Discuss your monitoring needsBuild production resilience with monitoring that detects equipment issues early and enables proactive maintenance.
Discuss your monitoring needsOur Clients
Your production uptime determines profitability and customer satisfaction. When equipment fails unexpectedly, you face costly downtime, missed deliveries, and emergency repair expenses. Our machine monitoring system can help you track equipment performance continuously, detect anomalies before failures occur, and visualize real-time data. This would help you maintain schedules, reduce maintenance costs, and improve OEE.
Softweb Solutions delivers machine monitoring systems integrating with ERP, MES, and OEE platforms. Our 120+ AI and IoT specialists bring more than 21 years of manufacturing expertise to build solutions connecting industrial equipment, processing sensor data in real time, and delivering actionable insights. We design systems that scale across production lines and drive measurable operational improvements.
We build monitoring systems that address specific operational needs across your factory floor. Each solution connects with your equipment to collect performance data and deliver insights that improve production outcomes.
Track equipment health parameters continuously to detect degradation patterns that indicate potential failures. This enables maintenance teams to intervene before breakdowns disrupt production schedules.
Measure production output, cycle times, and efficiency metrics to identify bottlenecks and optimization opportunities. This helps to detect where equipment operates below capacity and where improvements deliver value.
Capture the state in which equipment is currently operating like running, idle, down, and maintenance modes. This provides factory floor visibility that helps supervisors respond quickly to issues and optimize resource allocation.
Vibration patterns help measure and analyze the oscillation and movement of equipment. Monitoring these patterns enables early detection of wear in bearings, misalignment, and imbalance conditions.
Tracking thermal patterns helps maintenance teams address issues such as overheating or undercooling before they cause equipment damage.
Pressure deviations signal leaks, blockages, or component failures in hydraulic and pneumatic systems. Real-time pressure tracking prevents production interruptions from system malfunctions.
Monitoring of electrical components helps optimize energy usage and identify electrical anomalies. This safeguards the electrical parts against power quality issues and ensures reliable machine performance.
Oil condition reflects internal equipment wear and contamination levels. Regular oil analysis integrated with monitoring systems provides early warning of component degradation and lubrication problems.
Sound patterns reveal equipment health changes that other sensors may miss. Acoustic analysis detects issues like bearing noise, cavitation, or mechanical interference early in the failure process.
Tracking motor health allows early detection of potential problems related to electrical supply, mechanical condition, and overall system efficiency.
Track machine performance to enable immediate response to problems. This continuous visibility prevents small issues from becoming production disruptions that impact delivery schedules.
Review past performance to identify trends, patterns, and improvement opportunities. Historical analysis reveals recurring problems, seasonal variations, and the effectiveness of process changes over time.
Connect jobs, operators, and materials automatically through RFID technology. This eliminates manual data entry errors and ensures accurate tracking of work orders, labor time, and material consumption.
Receive notifications when specific conditions require attention. Customizable alerts ensure relevant stakeholders get timely information about problems, maintenance needs, or quality issues.
Track, monitor, and ensure the quality of products using digital tools and real-time data This provides visibility into quality performance and enables early detection of process variations that could produce defects.
Minimize costly mistakes through automated program loading and work instruction delivery. This reduces scrap from wrong programs, incorrect setups, or missed process steps that cause quality problems.
Our system connects to your machines through standard industrial communication protocols. These connections enable sensors to capture temperature, vibration, pressure, and cycle counts on CNCs and production lines. Automated collection records state of machine and its operating conditions continuously without operator input.
Once the sensor data gets collected, it is transmitted to edge devices and cloud platforms over secure industrial networks. Edge devices process critical data locally to enable immediate responses, while comprehensive datasets flow to cloud systems for deeper analysis. This two-layer approach delivers both fast alerts and complete analysis.
Building on transmitted data, analytics engines calculate OEE, availability, performance, and quality from the streams. Machine learning algorithms then compare live behavior with baselines to detect anomalies. This analysis helps system to expose bottlenecks, equipment wear, and variations that affect output.
After analysis, the system generates specific recommendations for operators, maintenance teams, and managers. Dashboards visualize current performance alongside historical trends to reveal patterns. The system highlights factors affecting productivity and ranks improvement opportunities by potential impact.
After deployment, the system learns from ongoing operations to refine models and baselines. It adapts to seasonality and product mix changes automatically. These adjustments preserve detection accuracy as the plant evolves, sustaining performance gains over time.
Increased machine utilization and efficiency
Monitor usage patterns to maximize productive time
Reduced downtime
Detect failures early to minimize interruptions
Improves performance and productivity
Identify bottlenecks limiting output rates
Facilitates true root cause analysis
Trace problems to underlying equipment issues
Inventory and resource optimization
Track consumption to reduce waste and costs
Refined maintenance schedules
Service equipment based on actual condition data
Yes, our machine monitoring systems integrate with existing ERP, MES, and OEE platforms through standard industrial protocols and APIs. The system fetches work orders and schedules, and publishes status, quality, and performance data. This creates a single view across planning, execution, and monitoring without replacing existing systems.
Yes, we support hybrid deployments that combine edge computing for real-time processing with cloud analytics for comprehensive insights. Edge devices handle time critical logic like alerts and immediate checks, while cloud platforms store history, run advanced analytics, and enable remote access. This architecture balances fast responses and deeper insight across your monitoring needs.
Implementation timelines vary with facility size, equipment mix, and integration scope. When data and networks are ready, sensors fit, and vendors grant access, setup moves faster. Legacy adapters, security reviews, and change management add steps. The timeline includes equipment connection, system configuration, operator training, and validation testing before full production operation.
Our system connects to CNCs, molding and packaging machines, assembly lines, and robotic cells through standard protocols. We integrate temperature sensors, vibration monitors, pressure transducers, current sensors, flow meters, and position sensors. Both modern and legacy assets are supported through retrofitted sensors and data acquisition hardware.
We connect machining centers, mills, lathes, grinders, presses, injectors, and blow molding, assembly automation, packaging, conveyors, robots, welding, and material handling. Our architecture adapts to various equipment types and manufacturing processes across discrete manufacturing environments.
Machine monitoring systems are built using industrial IoT platforms, edge devices, cloud services, time series storage, analytics engines, machine learning, and dashboards. Azure IoT Hub, AWS IoT Core, MQTT, OPC UA, InfluxDB, Apache Spark, TensorFlow, and Power BI. This technology stack processes high-velocity sensor data while delivering real-time insights and historical analytics.
The system both reports failures immediately and predicts future failures through condition monitoring and machine learning. Predictive models analyze history to forecast failures from trends in vibration, temperature, or performance. This supports reactive fixes and proactive maintenance from the same system.
Computer vision is not necessary for basic machine monitoring but adds valuable capabilities for certain applications. Standard monitoring uses sensors and machine data to track performance and condition. Computer vision enhances monitoring by enabling quality inspection, tool wear detection, operator safety monitoring, and visual defect identification.
Automate decisions that drive seamless, uninterrupted operations
From downtime to uptime – automate your response and stay ahead of every mechanical interruption.
