Before working with Ayla Networks, Ozner was already using cloud connectivity to identify and solve water-filtration system malfunctions as well as to monitor filter cartridges for replacements.But, in June 2015, Ozner executives talked with Ayla about how the company might further improve its water systems with IoT technology. They liked what they heard from Ayla, but the executives needed to be sure that Ayla’s Agile IoT Platform provided the security and reliability Ozner required.

For global leaders in the industrial chiller manufacturing, reliability of the entire production process is of the utmost importance. Chillers are refrigeration systems that produce ice water to provide cooling for a process or industrial application. One of those leaders sought a way to respond to asset performance issues, even before they occur. The intelligence to guarantee maximum reliability of cooling devices is embedded (pre-alarming). A pre-alarming phase means that the cooling device still works, but symptoms may appear, telling manufacturers that a failure is likely to occur in the near future. Chillers who are not internet connected at that moment, provide little insight in this pre-alarming phase.

First, aircraft manufacturer have trouble monitoring the health of aircraft systems with health prognostics and deliver predictive maintenance insights. Second, aircraft manufacturer wants a solution that can provide an in-context advisory and align job assignments to match technician experience and expertise.

Mercedes-Benz and his partner GAZ chose Siemens to be its maintenance partner at a new engine plant in Yaroslavl, Russia. The new plant offers a capacity to manufacture diesel engines for the Russian market, for locally produced Sprinter Classic. In addition to engines for the local market, the Yaroslavl plant will also produce spare parts. Mercedes-Benz Russia and his partner needed a service partner in order to ensure the operation of these lines in a maintenance partnership arrangement. The challenges included coordinating the entire maintenance management operation, in particular inspections, corrective and predictive maintenance activities, and the optimizing spare parts management. Siemens developed a customized maintenance solution that includes all electronic and mechanical maintenance activities (Integral Plant Maintenance).

Fleet managers are turning to predictive analytics to stay on top of maintenance and mitigate part failures before they happen. However, managing the large amount of new data generated by vehicle sensors is challenging.

CPG had a compression asset failure that interrupted service and had the potential to create customer dissatisfaction.

The customer prides itself on excellent engineering and customer centric philosophy, allowing its customer’s minds to be at ease and not worry about machine failure. They can easily deliver the excellent maintenance services to their customers, but there are some processes that can be automated to deliver less downtime for the customer and more efficient maintenance schedules.

Hirotec needed to ensure continuous operations and to minimize unplanned downtime in its manufacturing facilities. Unplanned downtime is costly and compromises Hirotec's ability to deliver its goods to customers on time.

Ham-Let is a leading manufacturer of valves, fittings and instrumentation. One of the key challenges in this industry is identifying malfunctions and shutting off the appropriate valves before any damage can be done. However, it is often a demanding task to identify the cause of the malfunction and isolate the valves quickly. 

Oil and gas companies are having problems learning from the data to understand the different operational states and failure modes of assets, and uses this learning to provide adequate warning before failures occur so operators can plan for corrective actions thus optimizing their Operations and Maintenance budgets.

In order to maintain its share of the international market for thermoplastic elastomers AES recently expanded its Florida plant by adding a new production line. While the existing lines were operating satisfactorily using a PROVOX distributed control system with traditional analog I/O, AES wanted advanced technology on the new line for greater economy, efficiency, and reliability. AES officials were anxious to get this line into production to meet incoming orders, but two hurricanes slowed construction.

Asset to be monitored: Wire Compactor that produces Steel RebarCustomer Faced The Following Challenges:Dependent upon machine uptime.Pressure cylinders within the compactor fail to control compression and speed causing problems in binding the coil.Equipment failure occurs in the final stage of production causing the entire line to stop, can you say bottleneck?Critical asset unequipped with sensors to produce data.

Customer wanted a real-time, seamless, cloud based integration between the existing on premise and cloud based application using SOA technology on Oracle Fusion Middleware Platform, a Contingent Worker Solution to collect, track, manage and report information for on-boarding, maintenance and off-boarding of contingent workers using a streamlined and Integrated business process, and streamlining of integration to the back-end systems and multiple SaaS applications.

It is not a matter if a hydraulic hose will fail, but a matter of when. When hydraulic hoses fail on a garbage truck or on heavy machinery, Eaton's customers spend millions of dollars on hydraulic fluid cleanup, fines, remote service and medical costs that result from injuries.

TKE had a number of initiatives underway around the world to enable remote monitoring of its elevators. However, none of the solutions provided the data and insight required to move from a traditional reactive maintenance approach to one that is predictive and even preemptive, and all were challenged by information overload issues that limited their value. TKE wanted a solution that would enable it to anticipate and quickly resolve maintenance issues for the majority of the 1.2 million elevators it services across the globe.

Recognizing the recent advances in and affordability of predictive maintenance (PdM), the company decided to implement a PdM program in one of its facilities on a trial basis. Anticipating positive results, we hope to gradually pursue wider implementation.

Cavitation is a condition can occur in centrifugal pumps when there is a sudden reduction in fluid pressure. Pressure reduction lowers the boiling point of liquids, resulting in the production of vapor bubbles if boiling occurs. This is more likely to happen at the inlet of the pump where pressure is typically lowest. As the vapor bubbles move towards the outlet of the pump where pressure is higher, they rapidly collapse (return to a liquid state) resulting in shock waves that can damage pump components.

Manufacturing plants are running at a much higher capacity utilization than ever before. Often, it is a 24 hour a day, seven days a week operation. With downtime costing plants up to US$20,000 a minute, they cannot afford disruption to their processes. In fact, a single occurrence can cost a plant upwards of US$2 million.FANUC was struggling with lack of visibility into how their customers were leveraging FANUC equipment on the factory floor. The only insight was gained after a problem had already occurred resulting in costly downtime for the customer.

Approximately 17,000 wells in the customer's portfolio have beam pump artificial lift technology. While beam pump technology is relatively inexpensive compared to other artificial lift technology, beam pumps fail frequently, at rates ranging from 66% to 95% per year. Unexpected failures result in weeks of lost production, emergency maintenance expenses, and costly equipment replacements.

Train operators the world over are expected to work miracles, i.e. never to be late. So, with acute service and availability targets to meet, an efficient maintenance program is important. And data-enabled functionality is a must for Siemens. Reactive maintenance (after an incident) and routine, preventive maintenance with its visual inspections and scheduled exchange of components, are no longer enough. We’ve moved on to more cost-effective, condition-based, predictive maintenance. The actual condition of components is measured via the transfer and remote monitoring of diagnostic sensor data; data which is also used to analyse patterns and trends. This helps predict when a component is likely to fail, so it can be repaired before anything untoward happens. To ensure the commercial sustainability of this approach, Siemens needs to use and re-use existing data, creating a kind of ‘Internet of Trains’. Towards this end, they’re analysing sensor data in near real time, which means they can react very quickly, ensuring that customer transport services aren’t interrupted. “It is really difficult to define every issue before it impacts operations using only data from the trains”, Kress explains. However, recent success stories prove that everything is possible.

Unexpected failures of building equipment can result in significant problems for facility operators. For example, refrigeration system downtimes result in expensive loss of perishables for retailers, or drugs for pharmacies. HVAC system downtimes drive need for emergency maintenance activities, and result in reduced customer satisfaction.

Oseco were completely new to Industry 4.0, Oseco needed to find a lean and quick method for successfully implementing condition monitoring.

A leading oil & gas company and one of GE’s most trusted and innovative partners had no way of integrating independent equipment issue detection capabilities. The company was losing money as a result of unplanned downtime due to maintenance scheduling issues and outdated software systems. They believed that a well-designed combination of IT assets would generate stronger insight than they had, affording them the ability to monitor offshore equipment from an onshore facility using real-time insight and predictive analytics.

Common paper breaks consequently lead up to 60 minutes of downtime, delaying a potential $10K per hour of production value process. Thus, defective products cause financial and damage company's reputation. Improving quality and reducing defect rates can generate millions of dollars of revenue per year for your company.

Objectives • Create an innovative IT environment that supports the move toward a solution-provider business model • Enhance existing business processes and leverage the power of Big Data and predictive maintenance to become more proactive, customer oriented, and competitive • Leverage the SAP HANA® platform to transform and simplify the entire SAP® solution landscape

Schneider Electric Le Vaudreuil factory in France is recognized by the World Economic Forum as one of the world’s top nine most advanced “lighthouse” sites, applying Fourth Industrial Revolution technologies at large scale. It was experiencing machine-health and unplanned downtime issues on a critical machine within their manufacturing process. They were looking for a solution that could easily leverage existing machine data feeds, be used by machine operators without requiring complex setup or extensive training, and with a fast return on investment.

The usual practice for car repairs at a Porsche car dealership is to have a factory representative or regional engineer visit to help diagnose the problem, and sometimes a faulty assembly is shipped back to company HQ for damage analysis. All that costs time and money for customers and dealers alike. 

With an abundance of data and insufficient skilled resources to perform analysis, Nissan were keen to expand the benefits of using data to influence maintenance. It decided to embark on a Condition Based maintenance programme to reduce production downtime by up to 50% across thousands of diverse assets. It was attracted to Senseye by its strong prognostics offering underpinned by machine learning.

A large construction aggregate plant operates 10 high horsepower Secondary Crusher Drive Motors and associated conveyor belts, producing 600 tons of product per hour. All heavy equipment requires maintenance, but the aggregate producer’s costs were greatly magnified any time that the necessary maintenance was unplanned and unscheduled. The product must be supplied to the customers on a tight time schedule to fulfill contracts, avoid penalties, and prevent the loss of future business. Furthermore, a sudden failure in one of the drive motors would cause rock to pile up in unwanted locations, extending the downtime and increasing the costs.Clearly, preventative maintenance was preferable to unexpected failures. So, twice each year, the company brought in an outside vendor to attach sensors to the motors, do vibration studies, measure bearing temperatures and attempt to assess the health of the motors. But that wasn’t enough. Unexpected breakdowns continued to occur. The aggregate producer decided to upgrade to a Condition Based Monitoring (CBM) sensor system that could continually monitor the motors in real time, apply data analytics to detect changes in motor behavior before they developed into major problems, and alert maintenance staff via email or text, anywhere they happened to be.A wired sensor network would have been cost prohibitive. An aggregate plant has numerous heavy vehicles moving around, so any cabling would have to be protected. But the plant covers 400 acres, and the cable would have to be trenched to numerous locations. Cable wasn’t going to work. The aggregate producer needed a wireless solution.

One of the world’s largest exploration and production companies was operating as leanly as possible given the prolonged slump in oil prices. As such, an operational parameter wasn’t effectively monitored and equipment failure went unnoticed in the machines.