Machine Condition Monitoring

Description:

Early predictions on equipment malfunctions and service maintenance can be automatically scheduled ahead of an actual part failure by installing sensors inside equipment to monitor and send reports.

Solution-Outline :

Machine condition monitoring is used to determine the condition of a machine with the intent to forecast mechanical wear and failure. The predicted data provides health information of the machine and helps to predict machinery failure. The monitoring equipment tracks changes in temperature, vibration, and output of machines in order to detect imbalance, corrosion, wear, misalignment, sediment build-up, or poorly lubricated parts. Condition monitoring has gained importance in line with increased company focus on productivity and asset utilization. The need for eliminating unnecessary maintenance costs and catastrophic breakdowns in production processes is expected to continue to drive the adoption of condition monitoring solutions.

The global machine condition monitoring market can be divided on the basis of products, applications, and components. On the basis of products, the global machine conditioning monitoring market can be segmented, into thermography equipment, vibration monitoring equipment, corrosion monitoring equipment, ultrasound emission equipment, lubricating oil analysis equipment, and motor current signature analysis equipment.

The vibration-based condition monitoring equipment holds the leading place amongst all condition-based maintenance technologies. On and off-line vibration monitoring is extensively used by industries to perform continuous production processes. In addition to vibration monitoring, other techniques used for machine condition monitoring are wear analysis, thermography, performance monitoring, degradation by products, and physical testing.

Types of Machine Condition Monitoring
Each of the five main varieties of machine condition monitoring serves a different role.

Route-Based Monitoring
– Route-based monitoring involves a technician recording data intermittently with a handheld instrument. This data is then used for trending to determine if more advanced analysis is needed.

Portable Machine Diagnostics
– Portable machine diagnostics is the process of using portable equipment to monitor the health of machinery. Sensors are typically permanently attached to a machine and portable data acquisition equipment is used to read the data.

Factory Assurance Test
– Factory assurance test is used to verify that a finished product meets its design specifications and to determine possible failure modes of the device.

Online Machine Monitoring
– Online machine monitoring is the process of monitoring equipment as it runs. Data is acquired by an embedded device and transmitted to a main server for data analysis and maintenance scheduling.

Asset Efficiency

Description:

Asset Efficiency refers to the process of analyzing the health of an asset. The health of an asset in itself relates to the asset’s utility, its need to be replaced, and its need for maintenance. The Use Case is broken down into three key components:
-Monitoring: Tracking the actual health and viability of the asset
-Diagnostic Analysis: Comparing new, real-time data to relevant data from the past in order to detect any anomalies
-Prognostics: Given past data, algorithms are developed to determine the remaining useful life of an asset

Solution-Outline :

Many industries have assets that are critical to their business processes. Availability and efficiency of these assets directly impact service and business. Using predictive analytics, the Asset Efficiency Testbed aims to collect real-time asset information efficiently and accurately and run analytics to make the right decisions in terms of operations, maintenance, overhaul and asset replacement.

The Asset Efficiency Testbed monitors, controls and optimizes the assets holistically taking into consideration operational, energy, maintenance, service, and information efficiency and enhance their performance utilization.

Practically speaking, sensors are applied to the assets to gauge health on the edge. The retrieved data is then stored in the cloud where a variety of software can be utilized to leverage the acquired data, including data analytics software. Finally, an Application Enablement Platform (AEP) is used to connect the data to custom applications designed specifically for your business.

Benefits: Improvement in asset life, minimized downtime, heightened predictability of service
-OT CAPEX Reduction
-OPEX Reduction
-Customer Service Improvement